Weakly Supervised Joint Sentiment-Topic Detection from Text

Lin, Chenghua; He, Yulan; Everson, Richard; Rüger, Stefan

doi:10.1109/tkde.2011.48

Cited by 285 publications

(243 citation statements)

References 18 publications

Supporting

Mentioning

241

Contrasting

Unclassified

Order By: Relevance

“…However, it has been argued that sentiment in text is not always associated with individual words, but instead, through relations and dependencies between words, which often formulate sentiment [16]. In previous work, these relations are usually complied as a set of syntactic patterns (i.e., Part-of-Speech patterns) [25,16,24], common sense concepts [6], semantic concepts [21,8], or statistical topics [20,11].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Semantic Patterns for Sentiment Analysis of Twitter

Saif

Fernández

et al. 2014

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet-and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

show abstract

Section: Related Workmentioning

confidence: 99%

“…LDA is a state-of-the-art method that have been widely used to this end [4]. 1 For example, Lin et al [11] propose JST, a topic generative model based on LDA. JST extracts, not only the patterns (topics) of words in text, but also their associated sentiment.…”

Section: Related Workmentioning

confidence: 99%

Semantic Patterns for Sentiment Analysis of Twitter

Saif

Fernández

et al. 2014

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…A large portion of work concentrates on classifying a sentiment-bearing document according to its sentiment polarity, i.e. either positive or negative as a binary classification like [1], [2], [3], [9]. Most of this work rely on labeled corpora where documents are labeled as positive, negative prior to the training.…”

Section: Introductionmentioning

confidence: 99%

Joint Sentiment-Topic Detection from Text Document

Tuplondhe¹

2014

IJCATR

View full text Add to dashboard Cite

Automated tools are used to detect subjective information like attitudes, opinions and feelings. Such process is called as sentiment analysis. The Joint Sentiment-Detection (JST) model is the probabilistic model which is extension of Latent Dirichlet Allocation (LDA) model that detects sentiment and topic simultaneously from text. Supervised approaches to sentiment classification often fail to produce satisfactory results when applied to other domains while the JST model is weakly supervised in nature where supervision only comes from domain independent sentiment lexicon. Thus, makes JST model portable to other domains. The proposed system incorporates a small amount of domain independent prior knowledge which is sentiment lexicon to further improve the sentiment classification accuracy. It also carry out experiments and evaluates the model performance on different datasets.

show abstract

“…As a result, it is of great value to automatically analyze the reviews to extract topics, sentiments and the associations between them. This problem received surging attention in both academic and industry recently [15,19,13,5,12,10,16,14,17]. The problem is different from traditional sentiment classification [18], where only the overall sentiment of a review (i.e., document-level sentiment) is cared about.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the linguistic mechanisms of sentiment expression, especially embedded in specific topics, are of fewer studies. The widely adopted approach is to treat each sentiment polarity as a special topic in a topic model and infer them together with the ordinary topics [13,14,10,12]. This approach is effective in topic extraction.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Topic Model with Decomposed Prior

Chen

Sun

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

This paper deals with the problem of jointly mining topics, sentiments, and the association between them from online reviews in an unsupervised way. Previous methods often treat a sentiment as a special topic and assume a word is generated from a flat mixture of topics, where the discriminative performance of sentiment analysis is not satisfied. A key reason is that providing rich priors on the polarity of a word for the flat mixture is difficult as the polarity often depends on the topic. To solve the problem we propose a novel model. We decompose the generative process of a word's sentiment polarity to a two-level hierarchy: the first level determines whether a word is used as a sentiment word or just an ordinary topic word, and the second level (if the word is used as a sentiment word) determines the polarity of it. With the decomposition, we provide separate prior for the the first level to encourage the discrimination between sentiment words and ordinary topic words. This prior is relatively easy to obtain compared to the concrete prior of the word polarities. We construct the prior based on part-of-speech tags of words and embed the prior into the model. Experiments on four real online review data sets show that our model consistently outperforms previous methods in the task of sentiment analysis, and simultaneously performs well in the sub-tasks of discovering ordinary topics, sentiment-specific topics, and extracting topic-specific sentiment words.

show abstract

Weakly Supervised Joint Sentiment-Topic Detection from Text

Cited by 285 publications

References 18 publications

Semantic Patterns for Sentiment Analysis of Twitter

Semantic Patterns for Sentiment Analysis of Twitter

Joint Sentiment-Topic Detection from Text Document

Sentiment Topic Model with Decomposed Prior

Contact Info

Product

Resources

About