The new transformed read-write Web has resulted in a rapid growth of user generated content on the Web resulting into a huge volume of unstructured data. A substantial part of this data is unstructured text such as reviews and blogs. Opinion mining and sentiment analysis (OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2000–2016. For the scientometric mapping, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, types of authorship of papers on OMSA, collaboration patterns in publications on OMSA, most productive countries, institutions, journals and authors, citation patterns and an year-wise citation reference network, and theme density plots and keyword bursts in OMSA publications during the period. A somewhat detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexicon-based) used in these publications, levels (document, sentence or aspect-level) of sentiment analysis work done and major application areas of OMSA. The paper presents a detailed analytical mapping of OMSA research work and charts the progress of discipline on various useful parameters.

Analytical mapping of opinion mining and sentiment analysis research during 2000–2015

The development of IoT technologies and the massive admiration and acceptance of social media tools and applications, new doors of opportunity have been opened for using data analytics in gaining meaningful insights from unstructured information. The application of opinion mining and sentiment analysis (OMSA) in the era of big data have been used a useful way in categorizing the opinion into different sentiment and in general evaluating the mood of the public. Moreover, different techniques of OMSA have been developed over the years in different data sets and applied to various experimental settings. In this regard, this paper presents a comprehensive systematic literature review, aims to discuss both technical aspect of OMSA (techniques and types) and non-technical aspect in the form of application areas are discussed. Furthermore, this paper also highlighted both technical aspects of OMSA in the form of challenges in the development of its technique and non-technical challenges mainly based on its application. These challenges are presented as a future direction for research.

Sentiment Analysis of Big Data: Methods, Applications, and Open Challenges

The rapid increase in data on social media creates a need for mining such data to get valuable insights. The data type can be unstructured with large volumes. Sentiment analysis addresses such need by detecting opinions or emotions on the social media text. Sentiment analysis can be performed in various domains such as social, medical and industrial applications. This paper presents a survey about sentiment analysis addressing the different concepts in this area, problems and its solutions, available APIs, tools used and presenting a list of open challenges in this area.

Sentiment Analysis over Social Networks: An Overview

The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across different platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper offers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses different neural network-based approaches for analyzing sentiment. Finally, these different approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

Social media data consists of feedback, critiques and other comments that are posted online by internet users. Collectively, these comments may reflect sentiments that are sometimes not captured in traditional data collection methods such as administering a survey questionnaire. Thus, social media data offers a rich source of information, which can be adequately analyzed and understood. In this paper, we survey the extant research literature on sentiment analysis and discuss various limitations of the existing analytical methods. A major limitation in the large majority of existing research is the exclusive focus on social media data in the English language. There is a need to plug this research gap by developing effective analytic methods and approaches for sentiment analysis of data in non-English languages. These analyses of non-English language data should be integrated with the analysis of data in English language to better understand sentiments and address people-centric issues, particularly in multilingual societies. In addition, developing a high accuracy method, in which the customization of training datasets is not required, is also a challenge in current sentiment analysis. To address these various limitations and issues in current research, we propose a method that employs a new sentiment analysis scheme. The new scheme enables us to derive dominant valence as well as prominent positive and negative emotions by using an adaptive fuzzy inference method (FIM) with linguistics processors to minimize semantic ambiguity as well as multi-source lexicon integration and development. Our proposed method overcomes the limitations of the existing methods by not only improving the accuracy of the algorithm but also having the capability to perform analysis on non-English languages. Several case studies are included in this paper to illustrate the application and utility of our proposed method.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3222&context=soss_research

Issues of Social Data Analytics with a New Method for Sentiment Analysis of Social Media Data

Sentiment analysis of microblogging texts can facilitate both organisations’ public opinion monitoring and governments’ response strategies development. Nevertheless, most of the existing analysis methods are conducted on Twitter, lacking of sentiment analysis of Chinese microblogging Weibo, and they generally rely on a large number of manually annotated training or machine learning to perform sentiment classification, yielding with difficulties in application. This paper addresses these problems and employs a sentiment ontology model to examine sentiment analysis of Chinese microblogging. We conduct a sentiment analysis of all public microblogging posts about ‘7.23 Wenzhou Train Collision’ broadcasted by Sina microblogging users between 23 July and 1 August 2011. For every day in this time period, we first extract eight dimensions of sentiment expect, joy, love, surprise, anxiety, sorrow, angry, and hate, and then build fuzzy sentiment ontology based on HowNet and semantic similarity for sentiment analysis; we also establish computing methods of influence and sentiment of microblogging texts; and we finally explore the change of public sentiment after ‘7.23 Wenzhou Train Collision’. The results show that the established sentiment analysis method has excellent application, and the change of different emotional values can reflect the success or failure of guiding the public opinion by the government.

Sentiment analysis of Chinese microblogging based on sentiment ontology: a case study of ‘7.23 Wenzhou Train Collision’

With the growing availability and popularity of online reviews, consumers' opinions towards certain products or services are generated and spread over the Internet; sentiment analysis thus arises in response to the requirement of opinion seekers. Most prior studies are concerned with statistics-based methods for sentiment classification. These methods, however, suffer from weak comprehension of text-based messages at semantic level, thus resulting in low accuracy. We propose an ontology-based opinion-aware framework – EOSentiMiner – to conduct sentiment analysis for Chinese online reviews from a semantic perspective. The emotion space model is employed to express emotions of reviews in the EOSentiMiner, where sentiment words are classified into two types: emotional words and evaluation words. Furthermore, the former contains eight emotional classes, and the latter is divided into two opinion evaluation classes. An emotion ontology model is then built based on HowNet to express emotion in a fuzzy way. Base...

Wei Shi

Papers

Sentiment analysis of Chinese microblogging based on sentiment ontology: a case study of ‘7.23 Wenzhou Train Collision’

EOSentiMiner: an opinion-aware system based on emotion ontology for sentiment analysis of Chinese online reviews