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 | 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. |
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. |