The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.

Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review

The evolution of sentiment analysis—A review of research topics, venues, and top cited papers

What do Airbnb users care about? An analysis of online review comments

The State of OA: A large-scale analysis of the prevalence and impact of Open Access articles

Traditionally, Web of Science and Scopus have been the two most widely used databases for bibliometric analyses. However, during the last few years some new scholarly databases, such as Dimensions, have come up. Several previous studies have compared different databases, either through a direct comparison of article coverage or by comparing the citations across the databases. This article aims to present a comparative analysis of the journal coverage of the three databases (Web of Science, Scopus and Dimensions), with the objective to describe, understand and visualize the differences in them. The most recent master journal lists of the three databases is used for analysis. The results indicate that the databases have significantly different journal coverage, with the Web of Science being most selective and Dimensions being the most exhaustive. About 99.11% and 96.61% of the journals indexed in Web of Science are also indexed in Scopus and Dimensions, respectively. Scopus has 96.42% of its indexed journals also covered by Dimensions. Dimensions database has the most exhaustive journal coverage, with 82.22% more journals than Web of Science and 48.17% more journals than Scopus. This article also analysed the research outputs for 20 selected countries for the 2010–2018 period, as indexed in the three databases, and identified database-induced variations in research output volume, rank, global share and subject area composition for different countries. It is found that there are clearly visible variations in the research output from different countries in the three databases, along with differential coverage of different subject areas by the three databases. The analytical study provides an informative and practically useful picture of the journal coverage of Web of Science, Scopus and Dimensions databases.

The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis

This paper presents our experimental work on a new kind of domain specific feature-based heuristic for aspect-level sentiment analysis of movie reviews. We have devised an aspect oriented scheme that analyses the textual reviews of a movie and assign it a sentiment label on each aspect. The scores on each aspect from multiple reviews are then aggregated and a net sentiment profile of the movie is generated on all parameters. We have used a SentiWordNet based scheme with two different linguistic feature selections comprising of adjectives, adverbs and verbs and n-gram feature extraction. We have also used our SentiWordNet scheme to compute the document-level sentiment for each movie reviewed and compared the results with results obtained using Alchemy API. The sentiment profile of a movie is also compared with the document-level sentiment result. The results obtained show that our scheme produces a more accurate and focused sentiment profile than the simple document-level sentiment analysis.

Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification

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

This paper presents our experimental work on performance evaluation of the SentiWordNet approach for document-level sentiment classification of Movie reviews and Blog posts. We have implemented SentiWordNet approach with different variations of linguistic features, scoring schemes and aggregation thresholds. We used two pre-existing large datasets of Movie Reviews and two Blog post datasets on revolutionary changes in Libya and Tunisia. We have computed sentiment polarity and also its strength for both movie reviews and blog posts. The paper also presents an evaluative account of performance of the SentiWordNet approach with two popular machine learning approaches: Naive Bayes and SVM for sentiment classification. The comparative performance of the approaches for both movie reviews and blog posts is illustrated through standard performance evaluation metrics of Accuracy, F-measure and Entropy.

Sentiment analysis of Movie reviews and Blog posts

This paper presents our experimental results on performance evaluation of all the three approaches for document-level sentiment classification. We have implemented two Machine Learning based classifiers (Naive Bayes and SVM), the Unsupervised Semantic Orientation approach (SO-PMI-IR algorithm) and the SentiWordNet approaches for sentiment classification of movie reviews. We used two pre-existing large datasets and collected one of moderate size on our own. The paper primarily makes two useful contributions: (a) it presents a comprehensive evaluative account of performance of all the three available approaches on use with movie reviews, and (b) it presents a new modified Adjective+Adverb combine scheme of SentiWordNet approach.

Sentiment analysis of textual reviews; Evaluating machine learning, unsupervised and SentiWordNet approaches

Aspect-level sentiment analysis refers to sentiment polarity detection from unstructured text at a fine-grained feature or aspect level This paper presents our experimental work on aspect-level sentiment analysis of movie reviews Movie reviews generally contain user opinion about different aspects such as acting, direction, choreography, cinematography, etc We have devised a linguistic rule-based approach which identifies the aspects from movie reviews, locates opinion about that aspect and computes the sentiment polarity of that opinion using linguistic approaches The system generates an aspect-level opinion summary The experimental design is evaluated on datasets of two movies The results achieved good accuracy and shows promise for deployment in an integrated opinion profiling system

Rajesh Piryani

Papers

Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification

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

Sentiment analysis of Movie reviews and Blog posts

Sentiment analysis of textual reviews; Evaluating machine learning, unsupervised and SentiWordNet approaches

A Linguistic Rule-Based Approach for Aspect-Level Sentiment Analysis of Movie Reviews