An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

/pdf/opinion-mining-and-sentiment-analysis-3v5ya3qqiw.pdf

Opinion Mining and Sentiment Analysis

https://bura.brunel.ac.uk/bitstream/2438/9980/2/Fulltext.pdf

The Role of Text Pre-processing in Sentiment Analysis

Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions toward events or topics. Textual emotion-mining methods have various applications, including gaining information about customer satisfaction, helping in selecting teaching materials in e-learning, recommending products based on users emotions, and even predicting mental-health disorders. In surveys on sentiment analysis, which are often old or incomplete, the strong link between opinion mining and emotion mining is understated. This motivates the need for a different and new perspective on the literature on sentiment analysis, with a focus on emotion mining. We present the state-of-the-art methods and propose the following contributions: (1) a taxonomy of sentiment analysis; (2) a survey on polarity classification methods and resources, especially those related to emotion mining; (3) a complete survey on emotion theories and emotion-mining research; and (4) some useful resources, including lexicons and datasets.

Current State of Text Sentiment Analysis from Opinion to Emotion Mining

Text analytics is a field that lies on the interface of information retrieval, machine learning, and natural language processing, and this textbookcarefully covers a coherently organized framework drawn from these intersectingtopics. The chapters of this textbook is organized into three categories:- Basic algorithms: Chapters 1 through 7 discuss the classical algorithmsfor machine learning from text such as preprocessing, similaritycomputation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methodsfrom text when combined with different domains such as multimedia andthe Web. The problem of information retrieval and Web search is alsodiscussed in the context of its relationship with ranking and machinelearning methods.- Sequence-centric mining: Chapters 10 through 14 discuss varioussequence-centric and natural language applications, such as featureengineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and eventdetection. This textbook covers machine learning topics for text in detail. Since thecoverage is extensive, multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offercourses not just in text analytics but also from the broader perspective ofmachine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrialpractitioners working in these related fields. This textbook is accompanied with a solution manual forclassroom teaching.

Machine Learning for Text

Polarity shifting marked by various linguistic structures has been a challenge to automatic sentiment classification. In this paper, we propose a machine learning approach to incorporate polarity shifting information into a document-level sentiment classification system. First, a feature selection method is adopted to automatically generate the training data for a binary classifier on polarity shifting detection of sentences. Then, by using the obtained binary classifier, each document in the original polarity classification training data is split into two partitions, polarity-shifted and polarity-unshifted, which are used to train two base classifiers respectively for further classifier combination. The experimental results across four different domains demonstrate the effectiveness of our approach.

http://nlp.suda.edu.cn/~gdzhou/publication/liss2010_COLING_Sentiment%20Classification%20and%20Polarity%20Shifting.pdf

Sentiment Classification and Polarity Shifting

This paper reports a study in automatic sentiment classification, i.e., automatically classifying documents as expressing positive or negative sentiments/opinions. The study investigates the effectiveness of using SVM (Support Vector Machine) on various text features to classify product reviews into recommended (positive sentiment) and not recommended (negative sentiment). Compared with traditional topical classification, it was hypothesized that syntactic and semantic processing of text would be more important for sentiment classification. In the first part of this study, several different approaches, unigrams (individual words), selected words (such as verb, adjective, and adverb), and words labeled with part-of-speech tags were investigated. A sample of 1,800 various product reviews was retrieved from Review Centre (www.reviewcentre.com) for the study. 1,200 reviews were used for training, and 600 for testing. Using SVM, the baseline unigram approach obtained an accuracy rate of around 76%. The use of selected words obtained a marginally better result of 77.33%. Error analysis suggests various approaches for improving classification accuracy: use of negation phrase, making inference from superficial words, and solving the problem of comments on parts. The second part of the study that is in progress investigates the use of negation phrase through simple linguistic processing to improve classification accuracy. This approach increased the accuracy rate up to 79.33%.

https://dr.ntu.edu.sg/bitstream/10220/20045/1/na_sui_khoo.sentiment_classification.isko2004.pdf

Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews

This paper reports a study in automatic sentiment classification, i.e. automatically classifying documents as expressing positive or negative sentiments/opinions. The study investigates the effectiveness of using SVM (Support Vector Machine) and Decision Tree induction on various text features to classify product reviews into recommended (positive sentiment) and not recommended (negative sentiment). Compared with traditional topical classification, it was hypothesized that syntactic and semantic processing of text would be more important for sentiment classification. In this study, five different approaches, unigrams (individual words), part-of-speech tagging, association rules, use of negation, and use of WordNet synsets (identifying a set of synonyms) were investigated. A sample of 1,800 miscellaneous product reviews was retrieved from Review Centre (www.reviewcentre.com. 2003) for the study. 1,200 reviews were used for training, and 600 for validation. Using SVM, the baseline unigrams approach obtained an accuracy rate of 81.3%. The use of WordNet synsets obtained marginally better result of 81.7%. The other text features did not yield better results. Error analysis suggests 3 approaches for improving classification accuracy: making inference from superficial words, solving the problems of "comments on parts" and "negation". Finally, Decision Tree induction was used to generate a list of indicative words that can identify the polarity of articles."

/pdf/sentiment-classification-of-product-reviews-using-svm-and-2u7pto9683.pdf

Haiyang Sui

Papers

Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews

Sentiment Classification of Product Reviews Using SVM and Decision Tree Induction