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144
Exploiting Social Relations for Sentiment Analysis
- in Microblogging. Proc. WSDM
, 2013
"... Microblogging, like Twitter 1, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiment and opinions on various t ..."
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Cited by 45 (8 self)
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Microblogging, like Twitter 1, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely source that carries mass sentiment and opinions on various topics. Existing sentiment analysis approaches often assume that texts are independent and identically distributed (i.i.d.), usually focusing on building a sophisticated feature space to handle noisy and short messages, without taking advantage of the fact that the microblogs are networked data. Inspired by the social sciences findings that sentiment consistency and emotional contagion are observed in social networks, we investigate whether social relations can help sentiment analysis by proposing a Sociological Approach to handling Noisy and short Texts (SANT) for sentiment classification. In particular, we present a mathematical optimization formulation that incorporates the sentiment consistency and emotional contagion theories into the supervised learning process; and utilize sparse learning to tackle noisy texts in microblogging. An empirical study of two real-world Twitter datasets shows the superior performance of our framework in handling noisy and short tweets.
Aspect Extraction through Semi-Supervised Modeling
- PROCEEDINGS OF 50TH ANNUAL MEETING OF ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
, 2012
"... Aspect extraction is a central problem in sentiment analysis. Current methods either extract aspects without categorizing them, or extract and categorize them using unsupervised topic modeling. By categorizing, we mean the synonymous aspects should be clustered into the same category. In this paper, ..."
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Cited by 24 (8 self)
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Aspect extraction is a central problem in sentiment analysis. Current methods either extract aspects without categorizing them, or extract and categorize them using unsupervised topic modeling. By categorizing, we mean the synonymous aspects should be clustered into the same category. In this paper, we solve the problem in a different setting where the user provides some seed words for a few aspect categories and the model extracts and clusters aspect terms into categories simultaneously. This setting is important because categorizing aspects is a subjective task. For different application purposes, different categorizations may be needed. Some form of user guidance is desired. In this paper, we propose two statistical models to solve this seeded problem, which aim to discover exactly what the user wants. Our experimental results show that the two proposed models are indeed able to perform the task effectively.
New avenues in opinion mining and sentiment analysis
- Intelligent Systems, IEEE
, 2013
"... valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools. of information were friends and special-ized magazine or websites. Now, the “social web ” pr ..."
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Cited by 22 (1 self)
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valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools. of information were friends and special-ized magazine or websites. Now, the “social web ” provides new tools to efficiently create and share ideas with everyone connected to
Exploiting Topic based Twitter Sentiment for Stock Prediction
"... This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market. We first utilize a continuous Dirichlet Process Mixture model to learn the daily topic set. Then, for each topic we derive its sentiment according to its opinion words distribution to bu ..."
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Cited by 12 (2 self)
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This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market. We first utilize a continuous Dirichlet Process Mixture model to learn the daily topic set. Then, for each topic we derive its sentiment according to its opinion words distribution to build a sentiment time series. We then regress the stock index and the Twitter sentiment time series to predict the market. Experiments on real-life S&P100 Index show that our approach is effective and performs better than existing state-of-the-art non-topic based methods. 1
Mining contentions from discussions and debates
- In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’12, 841–849
"... Social media has become a major source of information for many applications. Numerous techniques have been proposed to analyze network structures and text contents. In this paper, we focus on fine-grained mining of contentions in discussion/debate forums. Contentions are perhaps the most important f ..."
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Cited by 11 (5 self)
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Social media has become a major source of information for many applications. Numerous techniques have been proposed to analyze network structures and text contents. In this paper, we focus on fine-grained mining of contentions in discussion/debate forums. Contentions are perhaps the most important feature of forums that discuss social, political and religious issues. Our goal is to discover contention and agreement indicator expressions, and contention points or topics both at the discussion collection level and also at each individual post level. To the best of our knowledge, limited work has been done on such detailed analysis. This paper proposes three models to solve the problem, which not only model both contention/agreement expressions and discussion topics, but also, more importantly, model the intrinsic nature of discussions/debates, i.e., interactions among discussants or debaters and topic sharing among posts through quoting and replying relations. Evaluation results using real-life discussion/debate posts from several domains demonstrate the effectiveness of the proposed models.
Modeling Review Comments
- in Proceedings of 50th Anunal Meeting of Association for Computational Linguistics (ACL-2012) (Accepted for
, 2012
"... Writing comments about news articles, blogs, or reviews have become a popular activity in social media. In this paper, we analyze reader comments about reviews. Analyzing review comments is important because reviews only tell the experiences and evaluations of reviewers about the reviewed products o ..."
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Cited by 11 (3 self)
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Writing comments about news articles, blogs, or reviews have become a popular activity in social media. In this paper, we analyze reader comments about reviews. Analyzing review comments is important because reviews only tell the experiences and evaluations of reviewers about the reviewed products or services. Comments, on the other hand, are readers ’ evaluations of reviews, their questions and concerns. Clearly, the information in comments is valuable for both future readers and brands. This paper proposes two latent variable models to simultaneously model and extract these key pieces of information. The results also enable classification of comments accurately. Experiments using Amazon review comments demonstrate the effectiveness of the proposed models. 1.
A meta-analysis of state-of-the-art electoral prediction from Twitter data
, 2012
"... NOTICE: This is the author’s version of a work accepted for publication by SAGE Publications. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may hav ..."
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Cited by 10 (1 self)
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NOTICE: This is the author’s version of a work accepted for publication by SAGE Publications. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was published
1 Topic-Based Sentiment Analysis for the Social Web: The role of Mood and Issue-Related Words 1
"... General sentiment analysis for the social web has become increasingly useful to shed light on the role of emotion in online communication and offline events in both academic research and data journalism. Nevertheless, existing general purpose social web sentiment analysis algorithms may not be optim ..."
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Cited by 7 (2 self)
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General sentiment analysis for the social web has become increasingly useful to shed light on the role of emotion in online communication and offline events in both academic research and data journalism. Nevertheless, existing general purpose social web sentiment analysis algorithms may not be optimal for texts focussed around specific topics. This article introduces two new methods, mood setting and lexicon extension, to improve the accuracy of topic-specific lexical sentiment strength detection for the social web. Mood setting allows the topic mood to determine the default polarity for ostensibly neutral expressive text. Topic-specific lexicon extension involves adding topic-specific words to the default general sentiment lexicon. Experiments with eight data sets show that both methods can improve sentiment analysis performance in corpora and are recommended when the topic focus is tightest.
Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
- Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (p. 2). ACM
, 2013
"... Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express senti-ments about specific topics or entiti ..."
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Cited by 6 (0 self)
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Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express senti-ments about specific topics or entities with different strengths and intensities, where these sentiments are strongly related to their per-sonal feelings and emotions. A number of methods and lexical re-sources have been proposed to analyze sentiment from natural lan-guage texts, addressing different opinion dimensions. In this arti-cle, we propose an approach for boosting Twitter sentiment classifi-cation using different sentiment dimensions as meta-level features. We combine aspects such as opinion strength, emotion and polarity indicators, generated by existing sentiment analysis methods and resources. Our research shows that the combination of sentiment dimensions provides significant improvement in Twitter sentiment classification tasks such as polarity and subjectivity.
The FLDA Model for Aspect-based Opinion Mining: Addressing the Cold Start Problem ABSTRACT
"... Aspect-based opinion mining from online reviews has attracted a lot of attention recently. The main goal of all of the proposed methods is extracting aspects and/or estimating aspect ratings. Recent works, which are often based on Latent Dirichlet Allocation (LDA), consider both tasks simultaneously ..."
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Cited by 5 (0 self)
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Aspect-based opinion mining from online reviews has attracted a lot of attention recently. The main goal of all of the proposed methods is extracting aspects and/or estimating aspect ratings. Recent works, which are often based on Latent Dirichlet Allocation (LDA), consider both tasks simultaneously. These models are normally trained at the item level, i.e., a model is learned for each item separately. Learning a model per item is fine when the item has been reviewed extensively and has enough training data. However, in reallife datasets such as those from Epinions.com and Amazon.com more than 90 % of items have less than 10 reviews, so-called cold start items. State-of-the-art LDA models for aspect-based opinion mining are trained at the item level and therefore perform poorly for cold start items due to the lack of sufficient training data. In this paper, we propose a probabilistic graphical model based on LDA, called Factorized LDA (FLDA), to address the cold start problem. The underlying assumption of FLDA is that aspects and ratings of a review are influenced not only by the item but also by the reviewer. It further assumes that both items and reviewers can be modeled by a set of latent factors which represent their aspect and rating distributions. Different from state-of-the-art LDA models, FLDA is trained at the category level and learns the latent factors using the reviews of all the items of a category, in particular the non cold start items, and uses them as prior for cold start items. Our experiments on three real-life datasets demonstrate the improved effectiveness of the FLDA model in terms of likelihood of the held-out test set. We also evaluate the accuracy of FLDA based on two applicationoriented measures.
