Abstract | This paper describes an approach to utilizing term weights for sentiment analysis tasks and shows how various term weighting schemes improve the performance of sentiment analysis systems. |
Abstract | Previously, sentiment analysis was mostly studied under data-driven and lexicon-based frameworks. |
Abstract | We propose to model term weighting into a sentiment analysis system utilizing collection statistics, contextual and topic-related characteristics as well as opinion-related properties. |
Introduction | The field of opinion mining and sentiment analysis involves extracting opinionated pieces of text, determining the polarities and strengths, and extracting holders and targets of the opinions. |
Introduction | Much research has focused on creating testbeds for sentiment analysis tasks. |
Introduction | Previous studies for sentiment analysis belong to either the data-driven approach where an annotated corpus is used to train a machine learning (ML) classifier, or to the lexicon-based approach where a pre-compiled list of sentiment terms is utilized to build a sentiment score function. |
Related Work | Sentiment analysis task have also been using various lexical, syntactic, and statistical features (Pang and Lee, 2008). |
Related Work | Also, syntactic features such as the dependency relationship of words and subtrees have been shown to effectively improve the performances of sentiment analysis (Kudo and Matsumoto, 2004; Gamon, 2004; Matsumoto et al., 2005; Ng et al., 2006). |
Related Work | While these features are usually employed by data-driven approaches, there are unsupervised approaches for sentiment analysis that make use of a set of terms that are semantically oriented toward expressing subjective statements (Yu and Hatzivassiloglou, 2003). |
Conclusion | The primary contribution of this paper is to propose and benchmark new methodologies for sentiment analysis . |
Experiments | Movies Reviews: This is a popular dataset in sentiment analysis literature (Pang et al., 2002). |
Experiments | 6.2 Sentiment Analysis with Lexical Knowledge |
Experiments | 6.3 Sentiment Analysis with Dual Supervision |
Introduction | In Section 4, we present a constrained model and computational algorithm for incorporating lexical knowledge in sentiment analysis . |
Related Work | We point the reader to a recent book (Pang and Lee, 2008) for an in-depth survey of literature on sentiment analysis . |
Related Work | In this section, we briskly cover related work to position our contributions appropriately in the sentiment analysis and machine learning literature. |
Related Work | (Goldberg and Zhu, 2006) adapt semi-supervised graph-based methods for sentiment analysis but do not incorporate lexical prior knowledge in the form of labeled features. |
Introduction | Recent advances in language technology, especially in sentiment analysis , promise to (partially) automate this task. |
Introduction | Sentiment analysis is often considered in the context of the following two tasks: |
Introduction | How can technology developed for sentiment analysis be applied to media analysis? |
Related Work | Much work has been done in sentiment analysis . |
Related Work | We discuss related work in four parts: sentiment analysis in general, domain- and target-specific sentiment analysis , product review mining and sentiment retrieval. |
Related Work | 2.1 Sentiment analysis |
Abstract | Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierarchical relationships of products attributes is not fully utilized. |
Abstract | While this paper is mainly on sentiment analysis on reviews of one product, our proposed HL-SOT approach is easily generalized to labeling a mix of reviews of more than one products. |
Introduction | Faced with this problem, research works, e.g., (Hu and Liu, 2004; Liu et al., 2005; Lu et al., 2009), of sentiment analysis on product reviews were proposed and have become a popular research topic at the crossroads of information retrieval and computational linguistics. |
Introduction | Carrying out sentiment analysis on product reviews is not a trivial task. |
Introduction | We believe that labeling existing product reviews with attributes and corresponding sentiment forms an effective training resource to perform sentiment analysis . |
Abstract | Using a new multimodal dataset consisting of sentiment annotated utterances extracted from video reviews, we show that multimodal sentiment analysis can be effectively performed, and that the joint use of visual, acoustic, and linguistic modalities can lead to error rate reductions of up to 10.5% as compared to the best performing individual modality. |
Discussion | Video-level sentiment analysis . |
Experiments and Results | Second, previous work in subjectivity and sentiment analysis has demonstrated that a layered approach (where neutral statements are first separated from opinion statements followed by a separation between positive and negative statements) works better than a single three-way classification. |
Introduction | This is in line with earlier work on text-based sentiment analysis , where it has been observed that full-document reviews often contain both positive and negative comments, which led to a number of methods addressing opinion analysis at sentence level. |
Introduction | In our work, this dataset enabled a wide range of multimodal sentiment analysis experiments, addressing the relative importance of modalities and individual features. |
Introduction | The following section presents related work in text-based sentiment analysis and audiovisual emotion recognition. |
Related Work | In this section we provide a brief overview of related work in text-based sentiment analysis , as well as audiovisual emotion analysis. |
Related Work | 2.1 Text-based Subjectivity and Sentiment Analysis |
Related Work | The techniques developed so far for subjectivity and sentiment analysis have focused primarily on the processing of text, and consist of either rule-based classifiers that make use of opinion lexicons, or data-driven methods that assume the availability of a large dataset annotated for polarity. |
Abstract | Sentiment analysis in a multilingual world remains a challenging problem, because developing language-specific sentiment lexicons is an extremely resource-intensive process. |
Introduction | Sentiment analysis of English texts has become a large and active research area, with many commercial applications, but the barrier of language limits the ability to assess the sentiment of most of the world’s population. |
Introduction | 0 New Sentiment Analysis Resources — We have generated sentiment lexicons for 136 major languages via graph propagation which are now publicly availablel. |
Related Work | Sentiment analysis is an important area of NLP with a large and growing literature. |
Related Work | Excellent surveys of the field include (Liu, 2013; Pang and Lee, 2008), establishing that rich online resources have greatly expanded opportunities for opinion mining and sentiment analysis . |
Related Work | (2007) build up an English lexicon-based sentiment analysis system to evaluate the general reputation of entities. |
Abstract | In this paper, the problem of data sparsity in sentiment analysis , both monolingual and cross-lingual, is addressed through the means of clustering. |
Abstract | Experiments show that cluster based data sparsity reduction leads to performance better than sense based classification for sentiment analysis at document level. |
Abstract | Similar idea is applied to Cross Lingual Sentiment Analysis (CLSA), and it is shown that reduction in data sparsity (after translation or bilingual-mapping) produces accuracy higher than Machine Translation based CLSA and sense based CLSA. |
Clustering for Cross Lingual Sentiment Analysis | Given that sentiment analysis is a less resource intensive task compared to machine translation, the use of an MT system is hard to justify for performing |
Discussions | The reason for the drop in the accuracy of approach based on sense features for En-PD dataset is the domain specific nature of sentiment analysis (Blitzer et al., 2007), which is explained in the next point. |
Introduction | One such application is Sentiment Analysis (SA) (Pang and Lee, 2002). |
Introduction | In the current work, this particular insight is used to solve the data sparsity problem in the sentiment analysis by leveraging unlabelled monolingual corpora. |
Introduction | Popular approaches for Cross-Lingual Sentiment Analysis (CLSA) (Wan, 2009; Duh et al., 2011) depend on Machine Translation (MT) for converting the labeled data from one language to the other (Hiroshi et al., 2004; Banea et al., 2008; Wan, 2009). |
Related Work | There has been research related to clustering and sentiment analysis . |
Related Work | (2011) attempts to cluster features of a product to perform sentiment analysis on product reviews. |
Related Work | In situations where labeled data is not present in a language, approaches based on cross-lingual sentiment analysis are used. |
Abstract | We show that our structured regularizers consistently improve classification accuracies compared to standard regularizers that penalize features in isolation (such as lasso, ridge, and elastic net regularizers) on a range of datasets for various text prediction problems: topic classification, sentiment analysis , and forecasting. |
Conclusion | We empirically showed that models regularized using our methods consistently outperformed standard regularizers that penalize features in isolation such as lasso, ridge, and elastic net on a range of datasets for various text prediction problems: topic classification, sentiment analysis , and forecasting. |
Experiments | Sentiment analysis . |
Experiments | One task in sentiment analysis is predicting the polarity of a piece of text, i.e., whether the author is favorably inclined toward a (usually known) subject of discussion or proposition (Pang and Lee, 2008). |
Experiments | Sentiment analysis , even at the coarse level of polarity we consider here, can be confused by negation, stylistic use of irony, and other linguistic phenomena. |
Introduction | For tasks like text classification, sentiment analysis , and text-driven forecasting, this is an open question, as cheap “bag-of-words” models often perform well. |
Related and Future Work | For example, for the vote sentiment analysis datasets, latent variable models of Yessenalina et al. |
Structured Regularizers for Text | In sentence level prediction tasks, such as sentence-level sentiment analysis , it is known that most constituents (especially those that correspond to shorter phrases) in a parse tree are uninformative (neutral sentiment). |
Abstract | Finally, we show that, in both syntactic parsing and sentiment analysis , many broad linguistic trends can be captured via surface features. |
Conclusion | Moreover, we show that our parser is adaptable to other tree-structured tasks such as sentiment analysis ; we outperform the recent system of Socher et al. |
Introduction | (2013) demonstrates that sentiment analysis , which is usually approached as a flat classification task, can be viewed as tree-structured. |
Sentiment Analysis | One example is sentiment analysis . |
Sentiment Analysis | While approaches to sentiment analysis often simply classify the sentence monolithically, treating it as a bag of n-grams (Pang et al., 2002; Pang and Lee, 2005; Wang and Manning, 2012), the recent dataset of Socher et al. |
Sentiment Analysis | One structural difference between sentiment analysis and syntactic parsing lies in where the relevant information is present in a span. |
Approach | We develop a rich set of context-aware posterior constraints for sentence-level sentiment analysis by exploiting lexical and discourse knowledge. |
Approach | In this work, we explore coreference in the context of sentence-level sentiment analysis . |
Conclusion | While we focus on the sentence-level task, our approach can be easily extended to handle sentiment analysis at finer levels of granularity. |
Experiments | This demonstrates that our modeling of discourse information is effective and that taking into account the discourse context is important for improving sentence-level sentiment analysis . |
Introduction | The importance of discourse for sentiment analysis has become increasingly recognized. |
Introduction | Very little work has explored long-distance discourse relations for sentiment analysis . |
Introduction | Our work is the first to explore PR for sentiment analysis . |
Related Work | There has been a large amount of work on sentiment analysis at various levels of granularity (Pang and Lee, 2008). |
Related Work | Various attempts have been made to incorporate discourse relations into sentiment analysis : Pang and Lee (2004) explored the consistency of subjectivity between neighboring sentences; Mao and Lebanon (2007),McDonald et al. |
Abstract | The translation of sentiment information is a task from which sentiment analysis systems can benefit. |
Conclusion and Outlook | The automatic translation of this information could be beneficial for sentiment analysis in other languages. |
Conclusion and Outlook | Another important problem in sentiment analysis is the treatment of ambiguity. |
Introduction | Sentiment analysis is an important topic in computational linguistics that is of theoretical interest but also implies many real-world applications. |
Introduction | Usually, two aspects are of importance in sentiment analysis . |
Introduction | Work on sentiment analysis most often covers resources or analysis methods in a single language, usually English. |
Related Work | (2008) use machine translation for multilingual sentiment analysis . |
Sentiment Transfer | Although unsupervised methods for the design of sentiment analysis systems exist, any approach can benefit from using resources that have been established in other languages. |
Abstract | Most previous work on multilingual sentiment analysis has focused on methods to adapt sentiment resources from resource-rich languages to resource—poor languages. |
Conclusion | Another issue is to investigate how to improve multilingual sentiment analysis by exploiting comparable corpora. |
Introduction | The field of sentiment analysis has quickly attracted the attention of researchers and practitioners alike (e.g. |
Introduction | Indeed, sentiment analysis systems, which mine opinions from textual sources (e.g. |
Introduction | Previous work in multilingual sentiment analysis has therefore focused on methods to adapt sentiment resources (e.g. |
Related Work | Multilingual Sentiment Analysis . |
Related Work | There is a growing body of work on multilingual sentiment analysis . |
Abstract | This is problematic for sentiment analysis as they usually map words with similar syntactic context but opposite sentiment polarity, such as good and bad, to neighboring word vectors. |
Introduction | It is meaningful for some tasks such as pos-tagging (Zheng et al., 2013) as the two words have similar usages and grammatical roles, but it becomes a disaster for sentiment analysis as they have the opposite sentiment polarity. |
Introduction | In this paper, we propose learning sentiment-specific word embedding (SSWE) for sentiment analysis . |
Introduction | 0 We release the sentiment-specific word embedding learned from 10 million tweets, which can be adopted off-the-shell in other sentiment analysis tasks. |
Related Work | In the field of sentiment analysis , Bespalov et al. |
Related Work | This paper focuses on learning sentiment-specific word embedding, which is tailored for sentiment analysis . |
Related Work | (4) RAE: Recursive Autoencoder (Socher et al., 2011c) has been proven effective in many sentiment analysis tasks by learning compositionality automatically. |
Abstract | We propose a new concept, sentiment relevance, to make this distinction and argue that it better reflects the requirements of sentiment analysis systems. |
Conclusion | We introduced sentiment relevance to make this distinction and argued that it better reflects the requirements of sentiment analysis systems. |
Introduction | It is generally recognized in sentiment analysis that only a subset of the content of a document contributes to the sentiment it conveys. |
Introduction | Some sentiment analysis systems filter out objective language and predict sentiment based on subjective language only because objective statements do not directly reveal sentiment. |
Introduction | they are not optimal for sentiment analysis . |
Related Work | Many publications have addressed subjectivity in sentiment analysis . |
Related Work | As we argue above, if the goal is to identify parts of a document that are useful/non-useful for sentiment analysis , then S-relevance is a better notion to use. |
Related Work | Transfer learning has been applied previously in sentiment analysis (Tan and Cheng, 2009), targeting polarity detection. |
Introduction | Sentiment analysis (Pang and Lee, 2008) has been successfully applied to mine social media data for emotional responses to events, public figures, and consumer products just by using emotion lexicons—lists that map words to polarity values (+1 for positive sentiment, -l for negative) or valence values that try to capture degrees of polarity. |
Introduction | plays.1 2 Sentiment Analysis and Related Work |
Introduction | Sentiment analysis (SA) is now widely used commercially to infer user opinions from product reviews and social-media messages (Pang and Lee, |
Experimental results | In general, we argue that one should always consider modeling negators individually in a sentiment analysis system. |
Related work | Automatic sentiment analysis The expression of sentiment is an integral component of human language. |
Related work | Early work on automatic sentiment analysis includes the Widely cited work of (Hatzivas-siloglou and McKeown, 1997; Pang et al., 2002; Turney, 2002), among others. |
Related work | Much recent work considers sentiment analysis from a semantic-composition perspective (Moilanen and Pulman, 2007; Choi and Cardie, 2008; Socher et al., 2012; Socher et al., 2013), which achieved the state-of-the-art performance. |
Semantics-enriched modeling | (2013), which has showed to achieve the state-of-the-art performance in sentiment analysis . |
Semantics-enriched modeling | A recursive neural tensor network (RNTN) is a specific form of feed-forward neural network based on syntactic (phrasal-structure) parse tree to conduct compositional sentiment analysis . |
Semantics-enriched modeling | Figure 2: Prior sentiment-enriched tensor network (PSTN) model for sentiment analysis . |
Abstract | Automatic extraction of new words is an indispensable precursor to many NLP tasks such as Chinese word segmentation, named entity extraction, and sentiment analysis . |
Abstract | We also demonstrate how new sentiment word will benefit sentiment analysis . |
Introduction | Automatic extraction of new words is indispensable to many tasks such as Chinese word segmentation, machine translation, named entity extraction, question answering, and sentiment analysis . |
Introduction | pr Sentiment Analysis |
Introduction | New word detection is also important for sentiment analysis such as opinionated phrase extraction and polarity classification. |
Evaluation 111: Sentiment Analysis using ConnotationWordNet | Finally, to show the utility of the resulting lexicon in the context of a concrete sentiment analysis |
Evaluation 111: Sentiment Analysis using ConnotationWordNet | task, we perform lexicon-based sentiment analysis . |
Introduction | This word sense issue has been a universal challenge for a range of Natural Language Processing applications, including sentiment analysis . |
Introduction | Recent studies have shown that it is fruitful to tease out subjectivity and objectivity corresponding to different senses of the same word, in order to improve computational approaches to sentiment analysis (e.g. |
Related Work | There have been recent studies that address word sense disambiguation issues for sentiment analysis . |
Related Work | (2009) report a successful empirical result where WSD helps improving sentiment analysis, while Wiebe and Mihalcea (2006) study the distinction between objectivity and subjectivity in each different sense of a word, and their empirical effects in the context of sentiment analysis . |
Related Work | (2013) share this spirit by targeting more subtle, nuanced sentiment even from those words that would be considered as objective in early studies of sentiment analysis . |
Abstract | Sentiment analysis on Twitter data has attracted much attention recently. |
Approach Overview | Previous work (Barbosa and Feng, 2010; Davidiv et al., 2010) has discovered many effective features for sentiment analysis of tweets, such as emoticons, punctuation, prior subjectivity and polarity of a word. |
Conclusions and Future Work | Twitter sentiment analysis has attracted much attention recently. |
Introduction | In fact, it is easy to find many such cases by looking at the output of Twitter Sentiment or other Twitter sentiment analysis web sites. |
Introduction | In addition, tweets are usually shorter and more ambiguous than other sentiment data commonly used for sentiment analysis , such as reviews and blogs. |
Related Work | In recent years, sentiment analysis (SA) has become a hot topic in the NLP research community. |
Related Work | As Twitter becomes more popular, sentiment analysis on Twitter data becomes more attractive. |
Abstract | If SPM were yoked with sentiment analysis , we might identify which opinions belong to respected members of online communities or lay the groundwork for understanding how respect is earned in social networks. |
Abstract | Closely related natural language processing problems are authorship attribution, sentiment analysis , emotion detection, and personality classification: all aim to extract higher-level information from language. |
Abstract | Sentiment analysis , which strives to determine the attitude of an author from text, has recently garnered much attention (e.g. |
Background | Given a set of reviews, the tasks of sentiment analysis in ReNew are (l) splitting each review into segments, (2) associating each segment with a sentiment label (positive, neutral, negative), and (3) automatically generating a domain-specific sentiment lexicon. |
Conclusions and Future Work | The leading lexical approaches to sentiment analysis from text are based on fixed lexicons that are painstakingly built by hand. |
Conclusions and Future Work | In future work, we plan to apply ReNew to additional sentiment analysis problems such as review quality analysis and sentiment summarization. |
Experiments | We posit that EDUs are too fine-grained for sentiment analysis . |
Framework | They show that this pattern is a useful indicator for sentiment analysis . |
Introduction | High-quality sentiment lexicons can improve the performance of sentiment analysis models over general-purpose lexicons (Choi and Cardie, 2009). |
Abstract | Thwarting and sarcasm are two uncharted territories in sentiment analysis , the former because of the lack of training corpora and the latter because of the enormous amount of world knowledge it demands. |
Definition | This definition emphasizes thwarting as piggy-backing on sentiment analysis to improve the latter’s performance. |
Introduction | Although much research has been done in the field of sentiment analysis (Liu et al., 2012), thwarting and sarcasm are not addressed, to the best of our knowledge. |
Introduction | Thwarting has been identified as a common phenomenon in sentiment analysis (Pang et al., 2002, Ohana et al., 2009, Brooke, 2009) in various forms of texts but no previous work has proposed a solution to the problem of identifying thwarting. |
Results | The basic objective for creating a thwarting detection system was to include such a module in the general sentiment analysis framework. |
Results | Thus, using document polarity as a feature contradicts the objective of sentiment analysis , which is to find the document polarity. |
Introduction | In contrast, most of the previous work on sentiment analysis in social media does not consider these kinds of problems (Barbosa and Feng, 2010; Davidov et al., 2010; Speriosu et al., 2011). |
Introduction | We used the sentiment analyzer created by Kanayama and Na-sukawa (2012) to detect a phrase representing neg- |
Introduction | The sentiment analyzer can find not only sentiment phrases but the targets of the phrases based on syntactic parsing and the case framesl. |
Abstract | Furthermore, we introduce a manually annotated dataset for target-dependent Twitter sentiment analysis . |
Introduction | RNN utilizes the recursive structure of text, and it has achieved state-of-the-art sentiment analysis results for movie review dataset (Socher et al., 2012; Socher et al., 2013). |
Introduction | The recursive neural models employ the semantic composition functions, which enables them to handle the complex com-positionalities in sentiment analysis . |
Introduction | determines how to propagate the sentiments towards the target and handles the negation or intensification phenomena (Taboada et al., 2011) in sentiment analysis . |
Our Approach | In Section 3.2, we propose Adaptive Recursive Neural Network and use it for target-dependent sentiment analysis . |
Experiments | This suggests that full-fledged sentiment analysis is not effective at least in this setting. |
Introduction | An evident alternative to this approach is to use sentiment analysis (Mandel et al., 2012; Tsagkali-dou et al., 2011) assuming that problem reports should include something ‘bad’ while aid messages describe something ‘good’. |
Problem Report and Aid Message Recognizers | Word Sentiment Polarity (WSP) As we suggested before, full-fledged sentiment analysis to recognize the expressions, including clauses and phrases, that refer to something good or bad was not effective in our task. |
Problem Report and Aid Message Recognizers | To identify the sentiment polarity of words, we employed the word sentiment polarity dictionary used with a sentiment analysis tool for Japanese, the Opinion Extraction Tool software2, which is an implementation of Nakagawa et al. |
Problem Report and Aid Message Recognizers | Note that we used the Opinion Extraction Tool in the experiments to check the effectiveness of the full-fledged sentiment analysis in this task. |
Introduction | Note that the above problem is not only defined for Chinese sentiment classification, but also for various sentiment analysis tasks in other different languages. |
Related Work 2.1 Sentiment Classification | Corpus-based methods usually consider the sentiment analysis task as a classification task and they use a labeled corpus to train a sentiment classifier. |
Related Work 2.1 Sentiment Classification | Chinese sentiment analysis has also been studied (Tsou et al., 2005; Ye et al., 2006; Li and Sun, 2007) and most such work uses similar lexicon- |
Related Work 2.1 Sentiment Classification | To date, several pilot studies have been performed to leverage rich English resources for sentiment analysis in other languages. |
Empirical Evaluation | p@n is commonly used to evaluate a ranking when the total number of correct items is unknown (e.g., Web search results, aspect terms in topic models for sentiment analysis (Zhao et al., 2010), etc.). |
Introduction | AD-expressions are crucial for the analysis of interactive discussions and debates just as sentiment expressions are instrumental in sentiment analysis (Liu, 2012). |
Related Work | Sentiment analysis: Sentiment analysis determines positive and negative opinions expressed on entities and aspects (Hu and Liu, 2004). |
Related Work | Thus, this work expands the sentiment analysis research. |
Related Work | Also related are topic models in sentiment analysis which are often referred to as Aspect and Sentiment models (ASMs). |
Conclusion | For future work, we aim extend this work to constructing a multilingual sentiment analysis system and evaluate it with multilingual datasets such as product reviews collected from different countries. |
Introduction | There are multilingual subjectivity analysis systems available that have been built to monitor and analyze various concerns and opinions on the Internet; among the better known are OASYS from the University of Maryland that analyzes opinions on topics from news article searches in multiple languages (Cesarano et al., 2007)1 and TextMap, an entity search engine developed by Stony Brook University for sentiment analysis along with other functionalities (Bautin et al., 2008).2 Though these systems currently rely on English analysis tools and a machine translation (MT) technology to |
Introduction | Given sentiment analysis systems in different languages, there are many situations when the analysis outcomes need to be multilanguage-comparable. |
Related Work | To overcome the shortcomings of available resources and to take advantage of ensemble systems, Wan (2008) and Wan (2009) explored methods for developing a hybrid system for Chinese using English and Chinese sentiment analyzers . |
Background | Previously, RAE have successfully been applied to a number of tasks including sentiment analysis , paraphrase detection, relation extraction |
Experiments | The first task of sentiment analysis allows us to compare our CCG-conditioned RAE with similar, existing models. |
Experiments | 5.1 Sentiment Analysis |
Experiments | The effect of this was highlighted by the sentiment analysis task, with the more complex models performing |
Experiments | One of the challenging aspects of sentiment analysis of YouTube data is that the comments may express the sentiment not only towards the product shown in the video, but also the video itself, i.e., users may post positive comments to the video while being generally negative about the product and vice versa. |
Experiments | Given that the main goal of sentiment analysis is to select sentiment-bearing comments and identify their polarity, distinguishing between of f —t opic and spam categories is not critical. |
Related work | A recent study focuses on sentiment analysis for Twitter (Pak and Paroubek, 2010), however, their corpus was compiled automatically by searching for emoticons expressing positive and negative sentiment only. |
Related work | Most of the previous work on supervised sentiment analysis use feature vectors to encode documents. |
Introduction | We use sentiment analysis techniques to identify opinion expressions. |
Related Work | 2.1 Sentiment Analysis |
Related Work | Our work is related to a huge body of work on sentiment analysis . |
Related Work | A very detailed survey that covers techniques and approaches in sentiment analysis and opinion mining could be found in (Pang and Lee, 2008). |
Corpus Creation | Though there are many annotated data sets for the research of speech summarization and sentiment analysis , there is no corpus available for opinion summarization on spontaneous speech. |
Introduction | Both sentiment analysis (opinion recognition) and summarization have been well studied in recent years in the natural language processing (NLP) community. |
Introduction | Most of the previous work on sentiment analysis has been conducted on reviews. |
Introduction | However, this problem is challenging in that: (a) Summarization in spontaneous speech is more difficult than well structured text (Mckeown et al., 2005), because speech is always less organized and has recognition errors when using speech recognition output; (b) Sentiment analysis in dialogues is also much harder because of the genre difference compared to other domains like product reviews or news resources, as reported in (Raaijmakers et al., 2008); (c) In conversational speech, information density is low and there are often off topic discussions, therefore presenting a need to identify utterances that are relevant to the topic. |
Abstract | Taking inspiration from recent work in sentiment analysis that successfully models the compositional aspect of language, we apply a recursive neural network (RNN) framework to the task of identifying the political position evinced by a sentence. |
Introduction | In contrast, recent work in sentiment analysis has used deep learning to discover compositional effects (Socher et al., 201 lb; Socher et al., 2013b). |
Recursive Neural Networks | They have achieved state-of-the-art performance on a variety of sentence-level NLP tasks, including sentiment analysis , paraphrase detection, and parsing (Socher et al., 2011a; Hermann and Blunsom, 2013). |
Related Work | They use syntactic dependency relation features combined with lexical information to achieve then state-of-the-art performance on standard sentiment analysis datasets. |
Introduction | Sentiment analysis has recently received a lot of attention in the Natural Language Processing (NLP) community. |
Introduction | Polarity classification, whose goal is to determine whether the sentiment expressed in a document is “thumbs up” or “thumbs down”, is arguably one of the most popular tasks in document-level sentiment analysis . |
Introduction | (2007) have investigated a model for jointly performing sentence- and document-level sentiment analysis , allowing the relationship between the two tasks to be captured and exploited. |
Abstract | In experiments with text topic classification and sentiment analysis , we show that our method is both more scalable and more accurate than SSL techniques from previous work. |
Problem Definition | Our experiments demonstrate that MNB-FM outperforms previous approaches across multiple text classification techniques including topic classification and sentiment analysis . |
Problem Definition | In experiments across topic classification and sentiment analysis , MNB-FM was found to be more accurate and more scalable than several supervised and semi-supervised baselines from previous work. |
Introduction | In the past few years, opinion mining and sentiment analysis have attracted much attention in Natural Language Processing (NLP) and Information Retrieval (IR) (Pang and Lee, 2008; Liu, 2010). |
Introduction | In summary, we have three main contributions: 1) We give a systematic study on cross-domain sentiment analysis in word level. |
Introduction | There are also lots of studies for cross-domain sentiment analysis (Blitzer et al., 2007; Tan et al., 2007; Li et al., 2009; Pan et al., 2010; Bollegala et al., 2011; He et al., 2011; Glorot et al., 2011). |
Methods | (2009) introduced part-of-speech specific DAL features for sentiment analysis . |
Related Work | Sentiment analysis figures strongly in NLP work on news. |
Related Work | For deep representation of sentiment analysis , Ruppenhofer and Rehbein (2012) propose SentiFrameNet. |
Related Work 2.1 Market Prediction and Social Media | (2011) introduced a hybrid approach for stock sentiment analysis based on companies’ news articles. |
Related Work 2.1 Market Prediction and Social Media | LDA can learn a predefined number of topics and has been widely applied in its extended forms in sentiment analysis and many other tasks (Mei et al., 2007; Branavan et al., 2008; Lin and He, 2009; Zhao et al., 2010; Wang et al., 2010; Brody and Elhadad, 2010; Jo and Oh, 2011; Moghaddam and Ester, 2011; Sauper et al., 2011; Mukherjee and Liu, 2012; He et al., 2012). |
Related Work 2.1 Market Prediction and Social Media | In this work, we employ topic based sentiment analysis using DPM on Twitter posts (or tweets). |
Conclusion and Future Work | In the future, we will work on leveraging parallel sentences and word alignments for other tasks in sentiment analysis , such as building multilingual sentiment lexicons. |
Introduction | Sentiment Analysis (also known as opinion mining), which aims to extract the sentiment information from text, has attracted extensive attention in recent years. |
Introduction | Sentiment classification, the task of determining the sentiment orientation (positive, negative or neutral) of text, has been the most extensively studied task in sentiment analysis . |
Abstract | Aspect extraction is a central problem in sentiment analysis . |
Introduction | Aspect-based sentiment analysis is one of the main frameworks for sentiment analysis (Hu and Liu, 2004; Pang and Lee, 2008; Liu, 2012). |
Introduction | Our models are related to topic models in general (Blei et al., 2003) and joint models of aspects and sentiments in sentiment analysis in specific (e.g., Zhao et al., 2010). |
Introduction | They also tend to incorporate handcrafted lists of search terms to filter irrelevant content and use sentiment analysis lexicons for extracting opinion bias. |
Introduction | In this paper, we propose a generic method that aims to be independent of the characteristics described above (use of search terms or sentiment analysis tools). |
Related Work | majority of sentiment analysis tools are English-specific (or even American English) and, most importantly, political word lists (or ontologies) change in time, per country and per party; hence, generalisable methods should make an effort to limit reliance from such tools. |