Sentiment Analysis using Support Vector Machines with Diverse Information Sources

By Tony Mullen, Nigel Collier. 2004

804 citations recorded by the source corpus.

How researchers see this paper

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

Citations in the corpus

Attention-based LSTM for Aspect-level Sentiment Classification (2016, background): “Most of these studies focus on building sentiment classiﬁers with features, which include bag-of-words and sentiment lexicons, using SVM (Mullen and Collier, 2004).”
A machine learning approach to sentiment analysis in multilingual Web texts (2009, methodology): “The supervised techniques are applied mostly for recognizing the sentiment of complete documents (e.g., Pang et al. 2002, Finn and Kushmerick 2003, Mullen and Collier 2004, Salvetti et al. 2004, Gamon 2004, Aue and Gamon 2005, Bai et al. 2005).”
Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees (2005, methodology): “In the latest studies on document sentiment classiflcation, classiflers based on machine learning (e.g., [1], [6], [ 10 ]), which have been successful in other document classiflcation tasks, showed higher performance than rule-based classiflers.”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “Mullen and Collier [123] use the semantic orientation of words to create a feature space that is classified by SVM. Pak et al. [124] proposed a new sub-graph-based model.”
Which Side are You on? Identifying Perspectives at the Document and Sentence Levels (2006, result): “, (Pang et al., 2002; Morinaga et al., 2002; Turney and Littman, 2003; Nasukawa and Yi, 2003; Mullen and Collier, 2004; Beineke et al., 2004; Hu and Liu, 2004)) is perhaps the most similar to our work.”
Movie review mining and summarization (2006, unknown): “In 2004, Mullen and Collier integrated PMI values, Osgood semantic factors [10] and some syntactic relations into the features of SVM [9].”
Feature-based transfer learning with real-world applications (2010, background): “To date, there exist a lot of research work being proposed to improve the classification performance in the supervised setting [144, 128].”
News impact on stock price return via sentiment analysis (2014, methodology): “[24] build a model combining unigram features and favorability measures.”
A survey on sentiment detection of reviews (2009, methodology): “In fact, most of the literatures showed that SVM and Naı̈ve Bayes are perfect methods in single domain document sentiment classification (Aue and Gamon, 2005; Beineke et al., 2004; Kennedy and Inkpen, 2006; Lin et al., 2006; Matsumoto et al., 2005; Mullen and Collier, 2004; Pang and Lee, 2004; Pang ”
Lexicon-Based Methods for Sentiment Analysis (2011, background): “Although some of the machine-learning-based work makes use of linguistic features for training (Riloff and Wiebe 2003; Mullen and Collier 2004; Wiebe et al. 2004; Kennedy and Inkpen 2006; Ng, Dasgupta, and Niaz Ariﬁn 2006; Sokolova and Lapalme 2008), it nonetheless still suffers from lack of…”
A Study of Information Retrieval Weighting Schemes for Sentiment Analysis (2010, methodology): “…with more complex features such as structural or syntactic information (Wilson et al., 2005), favorability measures from diverse sources (Mullen and Collier, 2004), implicit syntactic indicators (Greene and Resnik, 2009), stylistic and syntactic feature selection (Abbasi et al., 2008),…”
Opinion Mining and Sentiment Analysis (2008, methodology): “Mullen and Collier [217] examine the effectiveness of various features based on topic (e.”
Using appraisal groups for sentiment analysis (2005, methodology): “We also show two results from Mullen and Collier [12], the only previous work we are aware of to use something akin to attitude type for sentiment analysis.”
Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts (2016, methodology): “Mullen and Collier (2004) combined various features and used SVM for classiﬁcation.”
Comparative Experiments on Sentiment Classification for Online Product Reviews (2006, methodology): “While discriminative models, such as SVM, have been employed in previous sentiment classifiers (e.g. (Mullen & Collier 2004)), it was not clear to us whether they are capable of processing online documents incrementally.”
A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “Support vector machines (SVM) SVM (Cortes and Vapnik 1995) is one of the most popular supervised classification methods whichhas a robust theoretical base and according to the research report (Liu 2007), is likely the most precisemethod in text classification, whichmakes it common in sentiment class”
Assigning Polarity Scores to Reviews Using Machine Learning Techniques (2005, methodology): “Recently, sentiment classification has been actively studied and experimental results have shown that machine learning approaches perform well (Pang, Lee, and Vaithyanathan 2002; Pang and Lee 2004; Mullen and Collier 2004; Turney 2002) .”