Title |
Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques
|
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Published in |
Cognitive Computation, June 2016
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DOI | 10.1007/s12559-016-9415-7 |
Pubmed ID | |
Authors |
Kia Dashtipour, Soujanya Poria, Amir Hussain, Erik Cambria, Ahmad Y. A. Hawalah, Alexander Gelbukh, Qiang Zhou |
Abstract |
With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing approaches on common data. Precision observed in our experiments is typically lower than the one reported by the original authors, which we attribute to the lack of detail in the original presentation of those approaches. Thus, we compare the existing works by what they really offer to the reader, including whether they allow for accurate implementation and for reliable reproduction of the reported results. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 67% |
United States | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Hungary | 1 | <1% |
Germany | 1 | <1% |
Unknown | 320 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 64 | 20% |
Student > Master | 46 | 14% |
Student > Bachelor | 27 | 8% |
Researcher | 18 | 6% |
Student > Doctoral Student | 18 | 6% |
Other | 39 | 12% |
Unknown | 110 | 34% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 135 | 42% |
Social Sciences | 16 | 5% |
Business, Management and Accounting | 10 | 3% |
Engineering | 9 | 3% |
Economics, Econometrics and Finance | 6 | 2% |
Other | 31 | 10% |
Unknown | 115 | 36% |