Index of papers in Proc. ACL 2009 that mention
  • sentiment analysis
Kim, Jungi and Li, Jin-Ji and Lee, Jong-Hyeok
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).
sentiment analysis is mentioned in 27 sentences in this paper.
Topics mentioned in this paper:
Li, Tao and Zhang, Yi and Sindhwani, Vikas
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.
sentiment analysis is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Wan, Xiaojun
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.
sentiment analysis is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Dasgupta, Sajib and Ng, Vincent
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.
sentiment analysis is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: