Abstract
This work deals with sentiment analysis on a corpus of French product reviews. We first introduce the corpus and how it was built. Then we present the results of two classification tasks that aimed at automatically detecting positive, negative and neutral reviews by using various machine learning techniques. We focus on methods that make use of feature selection techniques. This is done in order to facilitate the interpretation of the models produced so as to get some insights on the relative importance of linguistic items for marking sentiment and opinion. We develop this topic by looking at the output of the selection processes on various classes of lexical items and providing an explanation of the selection in argumentative terms.
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These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This research was supported in part by the Erasmus Mundus Action 2 program MULTI of the European Union, grant agreement number 2010-5094-7.
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Notes
- 1.
Some approaches use the presence of negation as a feature. This was experimented with, but it did not improve the results and it added a great number of features which slowed down the learning. Therefore it was abandoned.
- 2.
We ignore the borderline case of car (\(\approx \) because/since).
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Vincent, M., Winterstein, G. (2014). Argumentative Insights from an Opinion Classification Task on a French Corpus. In: Nakano, Y., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2013. Lecture Notes in Computer Science(), vol 8417. Springer, Cham. https://doi.org/10.1007/978-3-319-10061-6_9
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