Sentiment Analysis and Subjectivity

By Bing Liu. Handbook of Natural Language Processing, 2010

2,104 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/29366f39dc4237a380f20e9ae9fbeffa645200c9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. B. Liu
2. Bing Liu
3. Soujanya Poria
4. Erik Cambria
5. Lei Zhang
6. Zhiyuan Chen
7. Devamanyu Hazarika
8. Lei Shu

Citations in the corpus

• Mining customer requirements from online reviews: A product improvement perspective (2016, unknown): “These technologies have been extensively researched [22, 34, 35].”
• The impact of social and conventional media on firm equity value: A sentiment analysis approach (2013, background): “Sentiment analysis (SA) has evolved from TM and NLP, but aims to determine the sentiment of a speaker or a writer with respect to some specific topics [23].”
• Cross-Domain Co-Extraction of Sentiment and Topic Lexicons (2012, background): “In the past few years, opinion mining and sentiment analysis have attracted much attention in Natural Language Processing (NLP) and Information Retrieval (IR) (Pang and Lee, 2008; Liu, 2010).”
• A survey of sentiment analysis in social media (2018, background): “More detailed discussions about this problem also can be found in “ Sentiment Analysis and Subjectivity ” chapter of Handbook of Natural Language Processing [62].”
• Collaborative Multi-domain Sentiment Classification (2015, background): “Thus sentiment classification has been a hot topic in both industrial and academic fields [26], [17], [18], [31].”
• Identifying Top Sellers In Underground Economy Using Deep Learning-Based Sentiment Analysis (2014, background): “The former applies machine learning classifiers to determine the sentiment orientation; the latter determines the sentiment orientation based on the sentiment scores of each token given by a dictionary [8].”
• Measuring news sentiment (2020, background): “(5)See Liu (2010) for a detailed description of the ML approach to sentiment analysis.”
• Subjective Natural Language Problems: Motivations, Applications, Characterizations, and Implications (2011, background): “…language processing (NLP), current efforts on subjective natural language problems are concentrated on the vibrant field of opinion mining and sentiment analysis (Liu, 2010; T¨ackstr¨om, 2009), and ACL-HLT 2011 lists Sentiment Analysis, Opinion Mining and Text Clas-sification as a subject area.”
• Opinion Mining and Information Fusion: A survey (2016, background): “Some of these datasets are available in Kavita Ganesan's Blog, a Lillian Lee's homepage b and Bing Liu's website. c a http://www.text-analytics101.com/2011/07/user-review-datasets_20.html (Visited May 28, 2015) Additionally, most of the research in Information Fusion has been concerned with hard dat”
• Sentiment Analysis in MOOC Discussion Forums: What does it tell us? (2014, methodology): “Positive and negative terms used in this paper are defined by the sentiment lexicon from [14], a word list containing about 2,000 and 4,800 words marked as positive and negative, respectively.”
• Contextual semantics for sentiment analysis of Twitter (2016, background): “However, training data are difficult to obtain (Liu, 2010),”
• On Releasing Annotator-Level Labels and Information in Datasets (2021, background): “While all human judgments embed some degree of subjectivity, some tasks such as sentiment analysis (Liu et al., 2010), affect modeling (Alm, 2008; Liu et al., 2003), emotion detection (Hirschberg et al., 2003), and hate speech detection (Warner and Hirschberg, 2012) are agreed upon as relatively…”
• Techniques and applications for sentiment analysis (2013, unknown): “refer the reader to some of the extensive reviews written on this topic.(20,21) 27 In this review, I will focus on five”
• Unsupervised sentiment analysis with emotional signals (2013, methodology): “We follow the standard experiment settings [20] to evaluate the performance of sentiment analysis methods.”
• Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales (2014, background): “Depending on the problem examined, there are different types of sentiment analysis [31].”