Sentiment Classification of Online Customer Reviews and Blogs Using Sentence-level Lexical Based Semantic Orientation Method

ABSTRACT Sentiment analysis is the process of extracting knowledge from the peoples‟ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing efficient and effective analyses and classification of customer reviews, blogs and comments. The main inspiration for this thesis is to develop high performance domain independent sentiment classification method. This study focuses on sentiment analysis at the sentence level using lexical based method for different type data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for sentiment orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various data sets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at feedback level for blog comment

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Sentiment analysis and opinion mining from social media: A review

Ubiquitous presence of internet, advent of web 2.0 has made social media tools like blogs, Facebook, Twitter very popular and effective. People interact with each other, share their ideas, opinions, interests and personal information. These user comments are used for finding the sentiments and also add financial, commercial and social values. However, due to the enormous amount of user generated data, it is an expensive process to analyze the data manually. Increase in activity of opinion mining and sentiment analysis, challenges are getting added every day. There is a need for automated analysis techniques to extract sentiments and opinions conveyed in the user-comments. Sentiment analysis, also known as opinion mining is the computational study of sentiments and opinions conveyed in natural language for the purpose of decision making. Preprocessing data play a vital role in getting accurate sentiment analysis results. Extracting opinion target words provide fine-grained analysis on the customer reviews. The labeled data required for training a classifier is expensive and hence to over come, Domain Adaptation technique is used. In this technique, Single classifier is designed to classify homogeneous and heterogeneous input from di_erent domain. Sentiment Dictionary used to find the opinion about a word need to be consistent and a number of techniques are used to check the consistency of the dictionaries. This paper focuses on the survey of the existing methods of Sentiment analysis and Opinion mining techniques from social media

Is media just noise? The link between media factors and stock performance

PURPOSE OF THE STUDY Interest towards media analytics has increased significantly by both practitioners and academia alike. The hot topic is whether or not qualitative texts contain information relevant to stock financials, and if they do, whether the impact can be used to earn abnormal returns. In order to answer this, we study the impact media factors have on financial metrics in a novel specification that combines all the major media factors in a holistic media model. To transform qualitative texts information into a "sentiment score", we develop a new methodology to estimate sentiment more accurately than currently prevailing methods. DATA AND METHODOLOGY Our study focuses on the S&P 100 constituents between the time period of 2006 and 2011. As a source of qualitative texts, we use major news publications and earnings announcements retrieved from LexisNexis -database using a web scraper program developed for the purpose of this study. We retrieve the financials data for our study using Thomson Reuters Datastream -database. In order to estimate investor sentiment, we employ both the customary word count, as well as our novel Linearized Phrase-Structure -methodology. For word count, we test the Harvard Psychological -dictionary and a finance-specific dictionary by Loughran and McDonald (2011). As our data is panel in nature, we analyze the correlations in our error terms in line with Petersen (2009), first without clustering and then clustering by firm and by time. We find time-effect in our error terms, and therefore employ a Fama-Macbeth (1973) methodology with clustering done in quarters. To mitigate a methodological choice driving our results, we run our specifications with a multitude of alternative specifications. RESULTS We find that Linearized Phrase-Structure (LPS) outperforms the predominant naïve word count methodology. Also, we find that if employing word counts, researchers should employ context dependent dictionaries, such as Loughran and McDonald's (2011). In terms of our main variables, we find that the existing media factors are not mutually exclusive, and impact financial metrics in chorus. Alas, we do not find statistically significant relationship between sentiment and abnormal returns. However, we find a relationship between aggregate market news volume and abnormal returns, and also between sentiment and abnormal volatility. We infer that our findings support limited attention -theory, and provide evidence against market efficiency

Sentiment Classification of Online Customer Reviews and Blogs Using Sentence-level Lexical Based Semantic Orientation Method

ABSTRACT Sentiment analysis is the process of extracting knowledge from the peoples‟ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing efficient and effective analyses and classification of customer reviews, blogs and comments. The main inspiration for this thesis is to develop high performance domain independent sentiment classification method. This study focuses on sentiment analysis at the sentence level using lexical based method for different type data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for sentiment orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various data sets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at feedback level for blog comment

SENTIMENT CLASSIFICATION OF ONLINE CUSTOMER REVIEWS AND BLOGS USING SENTENCE-LEVEL LEXICAL BASED SEMANTIC ORIENTATION METHOD

Sentiment analysis is the process of extracting knowledge from the peoples’ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing efficient and effective analyses and classification of customer reviews, blogs and comments. The main inspiration for this thesis is to develop high performance domain independent sentiment classification method. This study focuses on sentiment analysis at the sentence level using lexical based method for different type data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for sentiment orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various datasets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at feedback level for blog comments