Results 1 - 10
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165
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 46 (9 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 31 (12 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 13 (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
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 12 (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.
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 12 (6 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.
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 12 (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
Vader: A parsimonious rule-based model for sentiment analysis of social media text
- In Proceedings of the Eighth Annual International Association for the Advancement of Artificial Intelligence Conference on Weblogs and Social Media. AAAI
, 2014
"... Abstract The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANE ..."
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Cited by 10 (0 self)
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Abstract The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a goldstandard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks.
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 9 (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.
