Building sentiment Lexicons applying graph theory on information from three Norwegian thesauruses

Sentiment lexicons are the most used tool to automatically predict sentiment in text. To the best of our knowledge, there exist no openly available sentiment lexicons for the Norwegian language. Thus in this paper we applied two different strategies to automatically generate sentiment lexicons for the Norwegian language. The first strategy used machine translation to translate an English sentiment lexicon to Norwegian and the other strategy used information from three different thesauruses to build several sentiment lexicons. The lexicons based on thesauruses were built using the Label propagation algorithm from graph theory. The lexicons were evaluated by classifying product and movie reviews. The results show satisfying classification performances. Different sentiment lexicons perform well on product and on movie reviews. Overall the lexicon based on machine translation performed the best, showing that linguistic resources in English can be translated to Norwegian without losing significant value

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

Semi-automatic approaches for exploiting shifter patterns in domain-specific sentiment analysis

This paper describes two different approaches to sentiment analysis. The first is a form of symbolic approach that exploits a sentiment lexicon together with a set of shifter patterns and rules. The sentiment lexicon includes single words (unigrams) and is developed automatically by exploiting labeled examples. The shifter patterns include intensification, attenuation/downtoning and inversion/reversal and are developed manually. The second approach exploits a deep neural network, which uses a pre-trained language model. Both approaches were applied to texts on economics and finance domains from newspapers in European Portuguese. We show that the symbolic approach achieves virtually the same performance as the deep neural network. In addition, the symbolic approach provides understandable explanations, and the acquired knowledge can be communicated to others. We release the shifter patterns to motivate future research in this direction

Sentiment analysis: the case of twitch chat - Mining user feedback from livestream chats

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementIn a world where users often share their thoughts and opinions through online communication channels, applications that can tap into these channels as to extract consumer feedback have become increasingly valuable. Traditional marketing research techniques such as interviews or surveys offer results that pale in comparison to sentiment analysis applications that can extract organic feedback from an extremely large selection, with very little resources and in real-time. This thesis focuses on proposing and developing one of these tools that targets livestreams, which have, over the years, seen a massive increase in popularity from both a user-base standpoint as well as brand involvement. We chose the livestreaming platform “Twitch” as the target of research and developed a sentiment analysis model, using rule-based approaches, capable of interpreting user chat messages and identifying whether those messages are negative, positive or neutral. Additionally, an application was developed to better view and analyze the results of the model. By segmenting our results by product reveal, we also exhibit how the application allows for the extraction of various insights about the public’s opinion of that product

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