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

Piryani, Rajesh; Madhavi, D.; Singh, Vivek Kumar

doi:10.1016/j.ipm.2016.07.001

Cited by 159 publications

(72 citation statements)

References 439 publications

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“…Shortly before the submission of the present paper, another bibliometric study on sentiment analysis was published by Piryani et al [66]. There are similarities but also many differences between our and their work.…”

Section: Discussionmentioning

confidence: 67%

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

Mäntylä

Graziotin

Kuutila

2018

Computer Science Review

516

231

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Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6,996 papers from Scopus. We find that the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990's. However, the outbreak of computer-based sentiment analysis only occurred with the availability of subjective texts on the Web. Consequently, 99% of the papers have been published after 2004. Sentiment analysis papers are scattered to multiple publication venues, and the combined number of papers in the top-15 venues only represent ca. 30% of the papers in total. We present the top-20 cited papers from Google Scholar and Scopus and a taxonomy of research topics. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software engineering and cyberbullying extend the utilization of sentiment analysis 1 .

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Section: Discussionmentioning

confidence: 67%

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

Mäntylä

Graziotin

Kuutila

2018

Computer Science Review

516

231

View full text Add to dashboard Cite

show abstract

“…The WoS database collection indexes documents of different types, namely, articles, reviews, proceedings papers, editorial material, and book reviews, in various languages (Piryani et al ., ). This study collects and analyses documents of all types that were written in English in the time period of 2012–2016.…”

Section: Materials Methods and Toolsmentioning

confidence: 97%

“…The Web of Science (WoS) database provides users access to a wide range of bibliographic and citation information from articles that were published in international journals over a long time period (Chang, 2016). The data were obtained from the Social Sciences Citation Index (SSCI) in the WoS database (version 5.24 -Web of Science Core Collection) on May 30, 2017. The WoS database collection indexes documents of different types, namely, articles, reviews, proceedings papers, editorial material, and book reviews, in various languages (Piryani et al, 2017). This study collects and analyses documents of all types that were written in English in the time period of 2012-2016.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

A Bibliometric Analysis of the Top Five Economics Journals During 2012–2016

Wei

2018

Journal of Economic Surveys

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This study presents a bibliometric analysis of the top five economics journals during the period 2012–2016 and provides helpful insights into the document types, the distribution of country/territories, the distribution of institutions, the geographical distribution of authors, the most active authors and their research interests or fields, the co‐authorship network, and global/local cooperation. This paper also provides valuable information about the core historical references and the hottest new papers through co‐citation analysis and citation analysis. A co‐word analysis based on the keywords and thematic noun‐phrases in the titles and abstracts of the sample papers was used to explore the hot research topics in the top five journals (e.g. ‘price’, ‘game’, ‘consumption’, ‘income’, ‘international trade’, ‘employment’, ‘monetary policy’, ‘welfare effects’ and ‘developing countries’). In addition, a comparative keyword analysis was used to explore the differences in the directions and characteristics of each journal and the changes in research focuses between ‘new’ and ‘old’ studies. The analytical methods of this study differ from those of reviews or previous studies, and the results fill the gaps not covered by those works.

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“…In previous studies [18,19], we have found that the use of sentiment scores and the search for positive and negative product features help in making decisions. However, we have not found studies that have combined product price, the quantitative star score given by users, the sentiment score given by an SA tool in a global review, and the sentiment score given for each specific extracted feature to classify the best products by brand or category shown on dashboards.…”

Section: Introductionmentioning

confidence: 99%

Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining

et al. 2019

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Companies have realized the importance of "big data" in creating a sustainable competitive advantage, and user-generated content (UGC) represents one of big data's most important sources. From blogs to social media and online reviews, consumers generate a huge amount of brand-related information that has a decisive potential business value for marketing purposes. Particularly, we focus on online reviews that could have an influence on brand image and positioning. Within this context, and using the usual quantitative star score ratings, a recent stream of research has employed sentiment analysis (SA) tools to examine the textual content of reviews and categorize buyer opinions. Although many SA tools split comments into negative or positive, a review can contain phrases with different polarities because the user can have different sentiments about each feature of the product. Finding the polarity of each feature can be interesting for product managers and brand management. In this paper, we present a general framework that uses natural language processing (NLP) techniques, including sentiment analysis, text data mining, and clustering techniques, to obtain new scores based on consumer sentiments for different product features. The main contribution of our proposal is the combination of price and the aforementioned scores to define a new global score for the product, which allows us to obtain a ranking according to product features. Furthermore, the products can be classified according to their positive, neutral, or negative features (visualized on dashboards), helping consumers with their sustainable purchasing behavior. We proved the validity of our approach in a case study using big data extracted from Amazon online reviews (specifically cell phones), obtaining satisfactory and promising results. After the experimentation, we could conclude that our work is able to improve recommender systems by using positive, neutral, and negative customer opinions and by classifying customers based on their comments.

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

Cited by 159 publications

References 439 publications

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

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

A Bibliometric Analysis of the Top Five Economics Journals During 2012–2016

Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining

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