Multi-aspect Sentiment Analysis with Topic Models

Lu, Bin; Ott, Myle; Cardie, Claire; Tsou, Benjamin K.

doi:10.1109/icdmw.2011.125

Cited by 176 publications

(112 citation statements)

References 19 publications

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“…For this reason, we select balanced datasets such as IMDB movie reviews (Maas et al 2011) that are used for many sentiment classification studies due to its balanced amount of data between positive and negative reviews. Twitter datasets (Mukherjee and Bhattacharyya 2012) and hotel review datasets (Lu et al 2011;Pontiki et al 2014) has been used in the previous studies and verified to be balanced datasets. Amazon review datasets contain reviews for small electronics and collected using script crawling for this research.…”

Section: Datamentioning

confidence: 99%

Data properties and the performance of sentiment classification for electronic commerce applications

Choi

Lee

2017

Inf Syst Front

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Sentiment classification has played an important role in various research area including e-commerce applications and a number of advanced Computational Intelligence techniques including machine learning and computational linguistics have been proposed in the literature for improved sentiment classification results. While such studies focus on improving performance with new techniques or extending existing algorithms based on previously used dataset, few studies provide practitioners with insight on what techniques are better for their datasets that have different properties. This paper applies four different sentiment classification techniques from machine learning (Naïve Bayes, SVM and Decision Tree) and sentiment orientation approaches to datasets obtained from various sources (IMDB, Twitter, Hotel review, and Amazon review datasets) to learn how different data properties including dataset size, length of target documents, and subjectivity of data affect the performance of those techniques. The results of computational experiments confirm the sensitivity of the techniques on data properties including training data size, the document length and subjectivity of training /test data in the improvement of performances of techniques. The theoretical and practical implications of the findings are discussed.

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Section: Datamentioning

confidence: 99%

Data properties and the performance of sentiment classification for electronic commerce applications

Choi

Lee

2017

Inf Syst Front

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“…Additionally, deep learning models have recently become very popular for Twitter sentiment analysis (Severyn and Moschitti, 2015). Topic modeling approaches for sentiment analysis can also be found in literature, e.g., (Mei et al, 2007;Lin and He, 2009;Lu et al, 2011;Alam et al, 2016;Rao, 2016) In this paper, we present systems submitted to the SemEval 2016 Task 4 (Nakov et al, 2016) that deal with the sentiment analysis of tweets on the sentence level. The submitted systems are based on the fusion of the different classifiers.…”

Section: Introductionmentioning

confidence: 99%

Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation

Palogiannidi

Kolovou

Christopoulou

et al. 2016

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the subtask B. Our system comprises of multiple independent models such as neural networks, semantic-affective models and topic modeling that are combined in a probabilistic way. The novelty of the system is the employment of a topic modeling approach in order to adapt the semantic-affective space for each tweet. In addition, significant enhancements were made in the main system dealing with the data preprocessing and feature extraction including the employment of word embeddings. Each model is used to predict a tweet's sentiment (positive, negative or neutral) and a late fusion scheme is adopted for the final decision.

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“…In a similar but more generic vein, must-sets and cannot-sets are used in MC-LDA (Chen et al, 2013b). Other related works include (Andrzejewski et al, 2011, Chen et al, 2013a, Chen et al, 2013c, Mukherjee and Liu, 2012, Hu et al, 2011, Jagarlamudi et al, 2012, Lu et al, 2011, Petterson et al, 2010. They all allow prior knowledge to be specified by the user to guide the modeling process.…”

Section: Introductionmentioning

confidence: 99%

Aspect Extraction with Automated Prior Knowledge Learning

Chen¹,

Mukherjee²,

Liu³

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

168

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Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the issue, several knowledge-based models have been proposed to incorporate prior knowledge provided by the user to guide modeling. In this paper, we take a major step forward and show that in the big data era, without any user input, it is possible to learn prior knowledge automatically from a large amount of review data available on the Web. Such knowledge can then be used by a topic model to discover more coherent aspects. There are two key challenges: (1) learning quality knowledge from reviews of diverse domains, and (2) making the model fault-tolerant to handle possibly wrong knowledge. A novel approach is proposed to solve these problems. Experimental results using reviews from 36 domains show that the proposed approach achieves significant improvements over state-of-the-art baselines.

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Multi-aspect Sentiment Analysis with Topic Models

Cited by 176 publications

References 19 publications

Data properties and the performance of sentiment classification for electronic commerce applications

Data properties and the performance of sentiment classification for electronic commerce applications

Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation

Aspect Extraction with Automated Prior Knowledge Learning

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