ARSA: a sentiment-aware model for predicting sales performance using blogs

By Yang Liu, Xiangji Huang, Aijun An, Xiaohui Yu. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2007

348 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/11543c44ed72784c656362b2ef42f7509250a423

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Citations in the corpus

Opinion Mining and Sentiment Analysis (2008, background): “Research employing probabilistic latent semantic analysis (PLSA) [125] or latent Dirichlet allocation (LDA) [39] can also be cast as language-modeling work [41, 194, 206].”
Towards detecting influenza epidemics by analyzing Twitter messages (2010, background): “[17] perform sentiment classification on blog posts, using the output to estimate sales performance of a product.”
Exploiting Social Relations and Sentiment for Stock Prediction (2014, methodology): “Various extensions of the traditional LDA model (Blei et al., 2003) has been proposed for modeling social media data (Wang et al., 2011, Jo and Oh, 2011; Liu et al., 2007; Mei et al., 2007; Diao et al., 2012).”
A Survey of Opinion Mining and Sentiment Analysis (2012, neutral): “[62] and Lu et al.”
Sentiment Analysis and Subjectivity (2010, background): “Other related works on feature extraction mainly use the ideas of topic modeling and clustering to capture topics/features in reviews [58, 59, 62, 89, 93, 106].”
Twitter mood predicts the stock market (2010, methodology): “Over the past 5 years significant progress has been made in sentiment tracking techniques that extract indicators of public mood directly from social media content such as blog content [10], [12], [15], [17] and in particular large-scale Twitter feeds [22].”
Modeling and Predicting the Helpfulness of Online Reviews (2008, methodology): “[11] assume that sentiment consists of multiple hidden aspects, and use a probability model to quantitatively measure the relationship between sentiment aspects and reviews.”
More than words: Social networks' text mining for consumer brand sentiments (2013, background): “In their seminal study on informal political texts, Malouf and Mullen (2008) argued that SA is a useful technique that might be used in order to analyze possible ideological biases, political opinions and political judgments’ favorability. The authors used this technique to investigate political ori”
A review of natural language processing techniques for opinion mining systems (2017, background): “Moreover, various practical applications of opinion mining, such as product pricing [3], competitive intelligence [4], market prediction [5, 6], election forecasting [7, 8], nation relationship analysis [9], and risk detection in banking systems [10], draw extensive attentions from industrial commun”
The impact of social and conventional media on firm equity value: A sentiment analysis approach (2013, background): “In addition, significant progress has been made in sentiment tracking techniques that extract indicators of public mood directly from socialmedia content such as blog content and in particular large-scale Twitter feeds [14,24,26,29].”