Learning Multilingual Subjective Language via Cross-Lingual Projections

By Rada Mihalcea, Carmen Banea, Janyce Wiebe. Annual Meeting of the Association for Computational Linguistics, 2007

468 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/60ece4f3b3f164af3377bbe4c09c430c362c1f31

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Citations in the corpus

A review of natural language processing techniques for opinion mining systems (2017, methodology): “it possible to exploit the abundant English resources [26, 27, 28, 29].”
Co-Training for Cross-Lingual Sentiment Classification (2009, methodology): “The above two methods have been used in (Banea et al., 2008) for Romanian subjectivity analysis, but the experimental results are not very promising.”
A survey on classification techniques for opinion mining and sentiment analysis (2017, background): “For example,Mihalcea et al. (2007) generates subjectivity analysis resources in a new language from English sentiment resources by using a bilingual dictionary.”
Sentiment Analysis and Opinion Mining (2012, methodology): “Mihalcea et al. (2007) also experimented with translating English subjectivity words and phrases into the target language. In fact, they tried two translation strategies for cross-language subjectivity classification. First, they derived a subjectivity lexicon for the new language (in their case, Ro”
Divergence of Sentiment and Stock Market Trading (2017, unknown): “This approach is expected to be more accurate for English than for other languages (Mihalcea et al., 2007).”
Using SentiWordNet for multilingual sentiment analysis (2008, background): “Mihalcea et al. compare in [ 3 ] a corpusbased approach and a lexicon-based approach to multilingual subjectivity analysis (subjective vs. objective).”
How Translation Alters Sentiment (2016, unknown): “Mihalcea, Banea, and Wiebe (2007) used English resources to automatically generate a Romanian subjectivity lexicon using an English–Romanian dictionary.”
Sentiment Analysis and Subjectivity (2010, background): “Sentiment classification is perhaps the most widely studied topic [3, 6, 8, 12, 13, 15, 16, 18, 27, 28, 33, 34, 35, 44, 45, 62, 64, 66, 67, 68, 70, 71, 73, 79, 80, 86, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 111].”
Multilingual Deep Learning (2013, background): “To overcome this problem of resource scarcity, recently there as been a lot of interest in reusing resources from a resource fortunate language to develop NLP capabilities in a resource deprived language [25] [6] [14] [23] [17] [15] [13] [10].”
Towards multimodal sentiment analysis: harvesting opinions from the web (2011, background): “While difficult problems such as cross-domain [5] or crosslanguage [16]portability have been addressed, not much has been done in terms of extending the applicability of sentiment analysis to other modalities, such as speech, gesture, or facial expressions.”
Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning (2016, background): “Mihalcea et al. (2007) translate English subjectivity words and phrases into the target language to build a lexicon-based classiﬁer.”
Opinion Mining and Sentiment Analysis (2008, background): “For instance, one can determine cross-lingual projections through bilingual dictionaries [209], or parallel corpora [159, 209].”
Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, methodology): “These two basic corpus-based methods have been used in Banea et al. (2008) for Romanian subjectivity analysis.”
Sentiment Analysis: An Overview from Linguistics (2016, unknown): “In some cases, the focus is a combination of different languages (Mihalcea et al. 2007; Banea et al. 2008, 2014; Popat et al. 2013).”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “However, there is interesting work on using sentiment analysis to generate text summaries (Ku, Liang, & Chen, 2006; Somprasertsri & Lalitrojwong, 2010) and on analyzing patterns of sentiment in social networks in novels and fairy tales (Nalisnick & Baird, 2013; Mohammad & Yang, 2011a; Davis &…”
An Autoencoder Approach to Learning Bilingual Word Representations (2014, background): “…past by projecting parameters learned from the annotated data of one language to another language (Yarowsky and Ngai, 2001; Das and Petrov, 2011; Mihalcea et al., 2007; Wan, 2009; Pad ´ o and Lapata, 2009) These projections are enabled by a bilingual resource such as a Machine Translation (MT)…”
Learning Sentiment Lexicons in Spanish (2012, methodology): “While (Mihalcea et al., 2007; Wan, 2008) have tried automatically translating sentiment lexicons developed for English using bilingual dictionaries, the accuracies obtained by these resources has been low.”