Techniques and applications for sentiment analysis

By Ronen Feldman. CACM, 2013

1,509 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9c42f726882b0ab4539afd34b546ae710aa1ceb0

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In Artificial Intelligence

Citations in the corpus

On the Practicality of Integrity Attacks on Document-Level Sentiment Analysis (2014, methodology): “[25] K. Thomas, C. Grier, D. Song, and V. Paxson, Suspended accounts in retrospect: an analysis of twitter spam, in Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference.”
Opinion Mining and Information Fusion: A survey (2016, background): “…the task of identifying positive and negative opinions, emotions and evaluations " [16], " the task of finding the opinions of authors about specific entities " [5], " tracking the mood of the public about a particular product or topic " [17], or simply " the task of polarity classification " [18].”
Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification (2014, methodology): “Under this assumption, many feature learning algorithms are proposed to obtain better classification performance (Pang and Lee, 2008; Liu, 2012; Feldman, 2013).”
Sentiment Embeddings with Applications to Sentiment Analysis (2016, methodology): “…context-based word embed-dings in many NLP tasks [14], we argue that they are not effective enough if directly applied to sentiment analysis [15], [16], [17], which is the research area targeting at extracting, analyzing and organizing the sentiment/opinion (e.g. thumbs up or thumbs down) of texts.”
Sentiment analysis in Facebook and its application to e-learning (2014, background): “Knowing the users' emotions is useful not only in the educational context but also in many others (e.g., marketing, politics, online shopping, and so on) (Feldman, 2013).”
A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods (2015, background): “To illustrate the growth of interest in the field, Figure 1 shows the steady growth on the number of searches on the topic, according to Google Trends [1] , mainly after the popularization of online social networks (OSNs).”
Improving the Interpretability of Neural Sentiment Classifiers via Data Augmentation (2019, background): “, 2016) and other NLP research topics (Feldman, 2013; Pang et al., 2002; Liu, 2012).”
Sentiment Analysis: An Overview from Linguistics (2016, positive): “One of the applications of sentiment analysis is in matching sentiment of markets and stocks to stock price (Feldman 2013).”
Sentiment analysis of twitter data using machine learning approaches and semantic analysis (2014, background): “Sentiment analysis is a process where the dataset consists of emotions, attitudes or assessment which takes into account the way a human thinks [1].”
Addressing Age-Related Bias in Sentiment Analysis (2018, background): “It is often used to measure opinions in product reviews or financial markets [32], which can inform and drive branding decisions, political campaign strategies, and automated financial trading systems [26].”
Natural language based financial forecasting: a survey (2017, methodology): “The Stock Sonar [37] used to conduct sentiment analysis at both the word level and phrase level.”
Sentiment analysis algorithms and applications: A survey (2014, background): “Cambria and Schuller et al. [9], Feldman [10] and Montoyo and Martı´nez-Barco [11] have given short surveys illustrating the new trends in SA.”
Multi-Hop Paragraph Retrieval for Open-Domain Question Answering (2019, background): “• Sentiment analysis (Feldman, 2013), a text categorization task that focuses on extracting the sentiment (positive or negative orientation) a writer expresses toward some subject.”
Beyond the hype: Big data concepts, methods, and analytics (2015, methodology): “While certain techniques categorize a document into two classes, negative and positive, others incorporate more sentiment classes (like the Amazon’s five-star system) (Feldman, 2013).”
Sentiment Analysis in Tourism: Capitalizing on Big Data (2019, methodology): “In the data mining process, different types of sentiment analysis methods can be distinguished in the literature; namely (i) machine learning, (ii) rule/dictionary-based and (iii) hybrid approaches (Feldman, 2013; Ribeiro et al., 2016).”