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112
SwatCS: Combining simple classifiers with estimated accuracy
"... This paper is an overview of the SwatCS system submitted to SemEval-2013 Task 2A: Contextual Polarity Disambiguation. The sentiment of individual phrases within a tweet are labeled using a combination of classifiers trained on a range of lexical features. The classifiers are combined by estimating t ..."
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This paper is an overview of the SwatCS system submitted to SemEval-2013 Task 2A: Contextual Polarity Disambiguation. The sentiment of individual phrases within a tweet are labeled using a combination of classifiers trained on a range of lexical features. The classifiers are combined by estimating the accuracy of the classifiers on each tweet. Performance is measured when using only the provided training data, and separately when including external data. 1
Inferring user preferences by probabilistic logical reasoning over social networks. arXiv preprint arXiv:1411.2679
, 2014
"... We propose a framework for inferring the latent attitudes or pref-erences of users by performing probabilistic first-order logical rea-soning over the social network graph. Our method answers ques-tions about Twitter users like Does this user like sushi? or Is this user a New York Knicks fan? by bui ..."
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We propose a framework for inferring the latent attitudes or pref-erences of users by performing probabilistic first-order logical rea-soning over the social network graph. Our method answers ques-tions about Twitter users like Does this user like sushi? or Is this user a New York Knicks fan? by building a probabilistic model that reasons over user attributes (the user’s location or gender) and the social network (the user’s friends and spouse), via inferences like homophily (I am more likely to like sushi if spouse or friends like sushi, I am more likely to like the Knicks if I live in New York). The algorithm uses distant supervision, semi-supervised data harvesting and vector space models to extract user attributes (e.g. spouse, edu-cation, location) and preferences (likes and dislikes) from text. The extracted propositions are then fed into a probabilistic reasoner (we investigate both Markov Logic and Probabilistic Soft Logic). Our experiments show that probabilistic logical reasoning significantly improves the performance on attribute and relation extraction, and also achieves an F-score of 0.791 at predicting a users likes or dis-likes, significantly better than two strong baselines.
E.: Emotex: Detecting emotions in twitter messages
- Academy of Science and Engineering (ASE
, 2014
"... Social media and microblog tools are increasingly used by individuals to express their feelings and opinions in the form of short text messages. Detecting emotions in text has a wide range of applications including identifying anx-iety or depression of individuals and measuring well-being or public ..."
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Social media and microblog tools are increasingly used by individuals to express their feelings and opinions in the form of short text messages. Detecting emotions in text has a wide range of applications including identifying anx-iety or depression of individuals and measuring well-being or public mood of a community. In this paper, we propose a new approach for automatically classifying text messages of individuals to infer their emotional states. To model emotional states, we utilize the well-established Circumplex model that characterizes affective experience along two di-mensions: valence and arousal. We select Twitter messages as input data set, as they provide a very large, diverse and freely avail- able ensemble of emotions. Using hash-tags as labels, our methodology trains supervised classifiers to de-tect multiple classes of emotion on potentially huge data sets with no manual effort. We investigate the utility of several features for emotion detection, including unigrams, emoticons, negations and punctuations. To tackle the prob-lem of sparse and high dimensional feature vectors of mes-sages, we utilize a lexicon of emotions. We have compared the accuracy of several machine learning algorithms, includ-ing SVM, KNN, Decision Tree, and Naive Bayes for classi-fying Twitter messages. Our technique has an accuracy of over 90%, while demonstrating robustness across learning algorithms.
Unitor: Combining syntactic and semantic kernels for twitter sentiment analysis
- In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013
, 2013
"... In this paper, the UNITOR system participat-ing in the SemEval-2013 Sentiment Analysis in Twitter task is presented. The polarity de-tection of a tweet is modeled as a classifica-tion task, tackled through a Multiple Kernel approach. It allows to combine the contribu-tion of complex kernel functions ..."
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In this paper, the UNITOR system participat-ing in the SemEval-2013 Sentiment Analysis in Twitter task is presented. The polarity de-tection of a tweet is modeled as a classifica-tion task, tackled through a Multiple Kernel approach. It allows to combine the contribu-tion of complex kernel functions, such as the Latent Semantic Kernel and Smoothed Par-tial Tree Kernel, to implicitly integrate syn-tactic and lexical information of annotated ex-amples. In the challenge, UNITOR system achieves good results, even considering that no manual feature engineering is performed and no manually coded resources are em-ployed. These kernels in-fact embed distri-butional models of lexical semantics to deter-mine expressive generalization of tweets. 1
GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent
"... This paper describes the details of our system submitted to the SemEval-2013 shared task on sentiment analysis in Twitter. Our approach to predicting the sentiment of Tweets and SMS is based on supervised machine learning techniques and task-specific feature engineering. We used a linear classifier ..."
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This paper describes the details of our system submitted to the SemEval-2013 shared task on sentiment analysis in Twitter. Our approach to predicting the sentiment of Tweets and SMS is based on supervised machine learning techniques and task-specific feature engineering. We used a linear classifier trained by stochastic gradient descent with hinge loss and elastic net regularization to make our predictions, which were ranked first or second in three of the four experimental conditions of the shared task. Furthermore, our system makes use of social media specific text preprocessing and linguistically motivated features, such as word stems, word clusters and negation handling. 1
USNA: A dual-classifier approach to contextual sentiment analysis
- in Proceedings of the 7th International Workshop on Semantic Evaluation, Task 1
, 2013
"... Abstract This paper describes a dual-classifier approach to contextual sentiment analysis at the SemEval-2013 Task 2. Contextual analysis of polarity focuses on a word or phrase, rather than the broader task of identifying the sentiment of an entire text. The Task 2 definition includes target word ..."
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Abstract This paper describes a dual-classifier approach to contextual sentiment analysis at the SemEval-2013 Task 2. Contextual analysis of polarity focuses on a word or phrase, rather than the broader task of identifying the sentiment of an entire text. The Task 2 definition includes target word spans that range in size from a single word to entire sentences. However, the context of a single word is dependent on the word's surrounding syntax, while a phrase contains most of the polarity within itself. We thus describe separate treatment with two independent classifiers, outperforming the accuracy of a single classifier. Our system ranked 6th out of 19 teams on SMS message classification, and 8th of 23 on twitter data. We also show a surprising result that a very small amount of word context is needed for high-performance polarity extraction.
Bootstrapped Learning of Emotion Hashtags #hashtags4you
"... We present a bootstrapping algorithm to automatically learn hashtags that convey emotion. Using the bootstrapping framework, we learn lists of emotion hashtags from unlabeled tweets. Our approach starts with a small number of seed hashtags for each emotion, which we use to automatically label tweets ..."
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We present a bootstrapping algorithm to automatically learn hashtags that convey emotion. Using the bootstrapping framework, we learn lists of emotion hashtags from unlabeled tweets. Our approach starts with a small number of seed hashtags for each emotion, which we use to automatically label tweets as initial training data. We then train emotion classifiers and use them to identify and score candidate emotion hashtags. We select the hashtags with the highest scores, use them to automatically harvest new tweets from Twitter, and repeat the bootstrapping process. We show that the learned hashtag lists help to improve emotion classification performance compared to an N-gram classifier, obtaining 8 % microaverage and 9 % macro-average improvements in F-measure. 1
CLex: A Lexicon for Exploring Color, Concept and Emotion Associations in Language
"... Existing concept-color-emotion lexicons limit themselves to small sets of basic emotions and colors, which cannot capture the rich pallet of color terms that humans use in communication. In this paper we begin to address this problem by building a novel, color-emotion-concept association lexicon via ..."
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Existing concept-color-emotion lexicons limit themselves to small sets of basic emotions and colors, which cannot capture the rich pallet of color terms that humans use in communication. In this paper we begin to address this problem by building a novel, color-emotion-concept association lexicon via crowdsourcing. This lexicon, which we call CLEX, has over 2,300 color terms, over 3,000 affect terms and almost 2,000 concepts. We investigate the relation between color and concept, and color and emotion, reinforcing results from previous studies, as well as discovering new associations. We also investigate cross-cultural differences in color-emotion associations between US and India-based annotators. 1
D.: Enhanced twitter sentiment classification using contextual information
- In: Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
, 2015
"... The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sen-timent classification. On the other hand, what tweets lack ..."
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The rise in popularity and ubiquity of Twitter has made sentiment analysis of tweets an important and well-covered area of research. However, the 140 character limit imposed on tweets makes it hard to use standard linguistic methods for sen-timent classification. On the other hand, what tweets lack in structure they make up with sheer volume and rich metadata. This metadata includes geolocation, temporal and author information. We hypothesize that sentiment is dependent on all these contextual factors. Different locations, times and authors have different emotional valences. In this paper, we explored this hypothesis by utilizing distant supervision to collect millions of labelled tweets from different locations, times and authors. We used this data to analyse the variation of tweet sentiments across different authors, times and locations. Once we explored and understood the relationship between these variables and sentiment, we used a Bayesian approach to combine these vari-ables with more standard linguistic fea-tures such as n-grams to create a Twit-ter sentiment classifier. This combined classifier outperforms the purely linguis-tic classifier, showing that integrating the rich contextual information available on Twitter into sentiment classification is a promising direction of research. 1
