Current State of Text Sentiment Analysis from Opinion to Emotion Mining

@article{Yadollahi2017CurrentSO,
  title={Current State of Text Sentiment Analysis from Opinion to Emotion Mining},
  author={Ali Yadollahi and Ameneh Gholipour Shahraki and Osmar R Zaiane},
  journal={ACM Computing Surveys (CSUR)},
  year={2017},
  volume={50},
  pages={1 - 33}
}
Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion mining involves the use of natural language processing and machine learning to determine the attitude of a writer towards a subject. Emotion mining is also using similar technologies but is concerned with detecting and classifying writers emotions… 

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References

SHOWING 1-10 OF 141 REFERENCES
Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu
TLDR
This comprehensive introduction to sentiment analysis takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions.
Sentiment Analysis and Opinion Mining
  • Lei Zhang, B. Liu
  • Computer Science
    Encyclopedia of Machine Learning and Data Mining
  • 2017
TLDR
This book is a comprehensive introductory and survey text that covers all important topics and the latest developments in the field with over 400 references and is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular.
Unsupervised sentiment analysis with emotional signals
TLDR
This work investigates whether the signals in social media can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation and incorporates the signals into an unsupervised learning framework for sentiment analysis.
Text-based emotion classification using emotion cause extraction
Joint sentiment/topic model for sentiment analysis
TLDR
A novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA) is proposed, called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text, which is fully unsupervised.
Sentiment analysis of blogs by combining lexical knowledge with text classification
TLDR
This paper presents a unified framework in which one can use background lexical information in terms of word-class associations, and refine this information for specific domains using any available training examples, and shows that this approach performs better than using background knowledge or training data in isolation.
Towards building a social emotion detection system for online news
Sentiment analysis: capturing favorability using natural language processing
This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole
Effectiveness of Simple Linguistic Processing in Automatic Sentiment Classification of Product Reviews
TLDR
Error analysis suggests various approaches for improving classification accuracy: use of negation phrase, making inference from superficial words, and solving the problem of comments on parts.
EmpaTweet: Annotating and Detecting Emotions on Twitter
TLDR
A corpus collected from Twitter with annotated micro-blog posts annotated at the tweet-level with seven emotions: ANGER, DISGUST, FEAR, JOY, LOVE, SADNESS, and SURPRISE is introduced.
...
...