A holistic lexicon-based approach to opinion mining

By Xiaowen Ding, B. Liu, Philip S. Yu. 2008

1,631 citations recorded by the source corpus.

One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning (2021, methodology): “The datasets are from 4 sources: (1) HL5Domains [17] with reviews of 5 products; (2) Liu3Domains [32] with reviews of 3 products; (3) Ding9Domains [9] with reviews of 9 products; and (4) SemEval14 with reviews of 2 products - SemEval 2014 Task 4 for laptop and restaurant.”
Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (2021, background): “The datasets are from 4 sources: (1) HL5Domains (Hu and Liu, 2004) with reviews of 5 products; (2) Liu3Domains (Liu et al., 2015) with reviews of 3 products; (3) Ding9Domains (Ding et al., 2008) with reviews of 9 products; and (4) SemEval14 with reviews of 2 products - Se-mEval 2014 Task 4 for lapto”
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (2021, background): “…HL5Domains (Hu and Liu, 2004): review sentences of 5 products; (2) Liu3Domains (Liu et al., 2015): review sentences of 3 products; (3) Ding9Domains (Ding et al., 2008): review sentences of 9 products; and (4) SemEval14: review sentences of 2 products - SemEval 2014 Task 4 for laptop and restaurant.”
Attentional Encoder Network for Targeted Sentiment Classification (2019, methodology): “Traditional machine learning methods, including rule-based methods (Ding et al., 2008) and statistic-based methods (Jiang et al.”
Why people hate your app: making sense of user feedback in a mobile app store (2013, background): “There are several pieces of past work analyzing reviews of other kinds of marketplace, such as markets of tangible commodity goods and movies [15, 7, 19, 4, 8, 11].”
Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation (2015, methodology): “…Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Filtering; I.2.7 [Artificial Intelligence]: Natural Language Processing; H.3.5 [Online Information Services]: Webbased services Keywords Personalized Recommendation; Collaborative Filtering; Sentiment Analysis; Text Mining”
Recurrent Attention Network on Memory for Aspect Sentiment Analysis (2017, methodology): “representative methods for this task include rulebased methods (Ding et al., 2008) and statisticbased methods (Jiang et al., 2011; Zhao et al., 2010). Ganapathibhotla and Liu (2008) extracted 2-tuples of (opinion target, opinion word) from”
Multi-entity Sentiment Scoring (2009, background): “27 which which 51.335 0 37 investor investors' 60.732 (4) [20] or FBS 4 [4] come with incomplete entity annotations as only some entities (e.g. sentiment roles or product features) are usually included per text region.”
Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, methodology): “Lexicon-based methods [11, 43, 45] utilize a lexicon of sentiment words to predict sentiment labels, whereas corpus-based methods [16, 29, 55] classify sentences to sentiment polarities using corpora that are labeled with sentiment labels.”
A Lexicon-based Approach for Hate Speech Detection (2015, background): “in [13] explored the idea of inter-sentential and intra-sentential sentiment consistency using natural language expressions.”
Combining lexicon-based and learning-based methods for twitter sentiment analysis (2011, methodology): “The approach of using opinion words (the lexicon) to determine opinion orientations is called the lexicon-based approach to sentiment analysis (Ding et al, 2008; Taboada, et al., 2010).”
Opinion Mining and Sentiment Analysis (2008, methodology): “rar ) and has been utilized in recent work [78].”
Explainable recommendation with fusion of aspect information (2018, background): “They explore user opinion [32, 35, 39, 40] from reviews by means of a sentiment lexicon [8, 36, 37] or corpus [14, 28, 44] and integrate these opinion information into recommendations.”
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions (2011, background): “, 2005), customer reviews (Ding et al., 2008) or multiple aspects of restaurants (Snyder and Barzilay, 2007).”