Sentiment analysis in a multilingual world remains a challenging problem, because developing language-specific sentiment lexicons is an extremely resource-intensive process.

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.

0 New Sentiment Analysis Resources — We have generated sentiment lexicons for 136 major languages via graph propagation which are now publicly availablel.

Sentiment analysis is an important area of NLP with a large and growing literature.

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 .

(2007) build up an English lexicon-based sentiment analysis system to evaluate the general reputation of entities.

Finally, we show that, in both syntactic parsing and sentiment analysis , many broad linguistic trends can be captured via surface features.

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.

(2013) demonstrates that sentiment analysis , which is usually approached as a flat classification task, can be viewed as tree-structured.

One example is 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.

One structural difference between sentiment analysis and syntactic parsing lies in where the relevant information is present in a span.

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.

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.

Sentiment analysis .

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).

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.

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.

For example, for the vote sentiment analysis datasets, latent variable models of Yessenalina et al.

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).

We develop a rich set of context-aware posterior constraints for sentence-level sentiment analysis by exploiting lexical and discourse knowledge.

In this work, we explore coreference in the context of sentence-level sentiment analysis .

While we focus on the sentence-level task, our approach can be easily extended to handle sentiment analysis at finer levels of granularity.

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 .

The importance of discourse for sentiment analysis has become increasingly recognized.

Very little work has explored long-distance discourse relations for sentiment analysis .

Our work is the first to explore PR for sentiment analysis .

There has been a large amount of work on sentiment analysis at various levels of granularity (Pang and Lee, 2008).

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.

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.

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.

In this paper, we propose learning sentiment-specific word embedding (SSWE) for sentiment analysis .

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.

In the field of sentiment analysis , Bespalov et al.

This paper focuses on learning sentiment-specific word embedding, which is tailored for sentiment analysis .

(4) RAE: Recursive Autoencoder (Socher et al., 2011c) has been proven effective in many sentiment analysis tasks by learning compositionality automatically.

Automatic extraction of new words is an indispensable precursor to many NLP tasks such as Chinese word segmentation, named entity extraction, and sentiment analysis .

We also demonstrate how new sentiment word will benefit sentiment analysis .

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 .

pr Sentiment Analysis

New word detection is also important for sentiment analysis such as opinionated phrase extraction and polarity classification.

Finally, to show the utility of the resulting lexicon in the context of a concrete sentiment analysis

task, we perform lexicon-based sentiment analysis .

This word sense issue has been a universal challenge for a range of Natural Language Processing applications, including sentiment analysis .

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.

There have been recent studies that address word sense disambiguation issues for sentiment analysis .

(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 .

(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 .

In general, we argue that one should always consider modeling negators individually in a sentiment analysis system.

Automatic sentiment analysis The expression of sentiment is an integral component of human language.

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.

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.

(2013), which has showed to achieve the state-of-the-art performance in sentiment analysis .

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 .

Figure 2: Prior sentiment-enriched tensor network (PSTN) model for sentiment analysis .

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.

The leading lexical approaches to sentiment analysis from text are based on fixed lexicons that are painstakingly built by hand.

In future work, we plan to apply ReNew to additional sentiment analysis problems such as review quality analysis and sentiment summarization.

We posit that EDUs are too fine-grained for sentiment analysis .

They show that this pattern is a useful indicator for sentiment analysis .

High-quality sentiment lexicons can improve the performance of sentiment analysis models over general-purpose lexicons (Choi and Cardie, 2009).

Furthermore, we introduce a manually annotated dataset for target-dependent Twitter sentiment analysis .

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).

The recursive neural models employ the semantic composition functions, which enables them to handle the complex com-positionalities in sentiment analysis .

determines how to propagate the sentiments towards the target and handles the negation or intensification phenomena (Taboada et al., 2011) in sentiment analysis .

In Section 3.2, we propose Adaptive Recursive Neural Network and use it for target-dependent sentiment analysis .

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.

In contrast, recent work in sentiment analysis has used deep learning to discover compositional effects (Socher et al., 201 lb; Socher et al., 2013b).

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).

They use syntactic dependency relation features combined with lexical information to achieve then state-of-the-art performance on standard sentiment analysis datasets.

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.

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.

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.

Most of the previous work on supervised sentiment analysis use feature vectors to encode documents.