A Review of Various Sentiment Analysis Techniques

This paper focuses on the utilization of sentiment analysis techniques in various application domains. Here we present major part of the research work done in the field of sentiment mining or opinion mining using the techniques and tools of sentiment analysis. We get a brief idea regarding the comparison of the techniques and the importance of the data set in acquiring the desired outcomes. This paper gives a comparison on the solutions presented in the research paper

Semi-supervised latent variable models for sentence-level sentiment analysis

We derive two variants of a semi-supervised model for fine-grained sentiment analysis. Both models leverage abundant natural supervision in the form of review ratings, as well as a small amount of manually crafted sentence labels, to learn sentence-level sentiment classifiers. The proposed model is a fusion of a fully supervised structured conditional model and its partially supervised counterpart. This allows for highly efficient estimation and inference algorithms with rich feature definitions. We describe the two variants as well as their component models and verify experimentally that both variants give significantly improved results for sentence-level sentiment analysis compared to all baselines

Is That Twitter Hashtag Worth Reading

Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life. Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion. In case of Twitter, popular information can be tracked using hashtags. Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others. In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays. We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not. We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents. A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed. The proposed technique helps twitter follower to read, relevant and interesting hashtag.Comment: 10 pages, 6 figures, Presented at the Third International Symposium on Women in Computing and Informatics (WCI-2015

Latent sentiment model for weakly-supervised cross-lingual sentiment classification

In this paper, we present a novel weakly-supervised method for crosslingual sentiment analysis. In specific, we propose a latent sentiment model (LSM) based on latent Dirichlet allocation where sentiment labels are considered as topics. Prior information extracted from English sentiment lexicons through machine translation are incorporated into LSM model learning, where preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. An efficient parameter estimation procedure using variational Bayes is presented. Experimental results on the Chinese product reviews show that the weakly-supervised LSM model performs comparably to supervised classifiers such as Support vector Machines with an average of 81% accuracy achieved over a total of 5484 review documents. Moreover, starting with a generic sentiment lexicon, the LSM model is able to extract highly domainspecific polarity words from text

Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise

Social media based digital epidemiology has the potential to support faster response and deeper understanding of public health related threats. This study proposes a new framework to analyze unstructured health related textual data via Twitter users' post (tweets) to characterize the negative health sentiments and non-health related concerns in relations to the corpus of negative sentiments, regarding Diet Diabetes Exercise, and Obesity (DDEO). Through the collection of 6 million Tweets for one month, this study identified the prominent topics of users as it relates to the negative sentiments. Our proposed framework uses two text mining methods, sentiment analysis and topic modeling, to discover negative topics. The negative sentiments of Twitter users support the literature narratives and the many morbidity issues that are associated with DDEO and the linkage between obesity and diabetes. The framework offers a potential method to understand the publics' opinions and sentiments regarding DDEO. More importantly, this research provides new opportunities for computational social scientists, medical experts, and public health professionals to collectively address DDEO-related issues.Comment: The 2017 Annual Meeting of the Association for Information Science and Technology (ASIST

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning

Mining Pros and Cons of Product Features from Online Reviews: Aspect-Sentiment Analysis on Textual Reviews

Online reviews have become the modern-day referral, which shapes consumers’ perceptions of products and thus influences product sales performance in the digital economy (Blanco, Sarasa, & Sanclemente, 2010). Prior literature suggests that online consumers\u27 textual information significantly affects product performance and has important strategic value for organizations (Zhou et al., 2018). Sentiment analysis is used to identify the positive and negative tone of textual information (Hu, Bose, Koh, & Liu, 2012) and has become a primary application of analytics when researchers investigate how user-generated information influences product performance. However, most existing online review studies conduct sentiment analysis at the review level, which focuses on identifying the valence of an individual message or review (e.g., Hu et al., 2012; Wu, Huang, & Zhao, 2019), rather than the feature-based, which aims to reveal prior customers’ evaluation of product features in reviews (e.g., Wang, Lu, & Tan, 2018). Since consumers’ fundamental purpose in reading textual reviews is to obtain details about the product attributes’ pros and cons (Xu, 2019), conducting sentiment analysis at review-level fails to measure customer satisfaction concerning each attribute of products or services and does not match the mechanism of how online text reviews are consumed. Therefore, feature-based review-level sentiment analysis better reflects the actual value of textual information in the digital economy