A survey on sentiment analysis challenges
By Doaa Hussein. Journal of King Saud University: Engineering Sciences, 2016
637 citations recorded by the source corpus.
Abstract With accelerated evolution of the internet as websites, social networks, blogs, online portals, reviews, opinions, recommendations, ratings, and feedback are generated by writers. This writer generated sentiment content can be about books, people, hotels, products, research, events, etc. These sentiments become very beneficial for businesses, governments, and individuals. While this content is meant to be useful, a bulk of this writer generated content require using the text mining techniques and sentiment analysis. But there are several challenges facing the sentiment analysis and evaluation process. These challenges become obstacles in analyzing the accurate meaning of sentiments and detecting the suitable sentiment polarity. Sentiment analysis is the practice of applying natural language processing and text analysis techniques to identify and extract subjective information from text. This paper presents a survey on the sentiment analysis challenges relevant to their approaches and techniques.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/d880b4e925b19ad6facc73cf4c6ce21e181f46d9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Hossein Hassani
- Maedeh Taj Mazinani
- Stephane Unger
- Nhan Cach Dang
- Fernando De la Prieta Pintado
- Christina Beneki
- María N. Moreno García
- Chaitanya Kulkarni
In Information Systems
- Hossein Hassani
- Maedeh Taj Mazinani
- Stephane Unger
- Christina Beneki
- Chaitanya Kulkarni
- Mohammad Reza Yeganegi
- Mayur Wankhade
- A. C. Rao
Citations in the corpus
- Text Mining in Big Data Analytics (2020, background): “Hussein [92] presented a survey on the challenges relevant to the approaches and techniques of sentiment analysis.”
- A survey on sentiment analysis methods, applications, and challenges (2022, background): “This style, which is dependent on benefits and drawbacks, is listed separately by the authors, and the pros and cons sections are typically comprised of brief sentences (Birjali et al. 2021; Hussein 2018; Ebrahimi et al. 2017; Mohammad 2017).”
- Sentiment Analysis Based on Deep Learning: A Comparative Study (2020, background): “analysis continue to face theoretical and technical issues that hinder their overall accuracy in polarity detection [3,4].”