Authors:
Randa Benkhelifa
1
and
Nasria Bouhyaoui
1
;
2
Affiliations:
1
Laboratoire de L’intelligence Artificielle et des Technologies de L’information, Université Kasdi Merbah, Route de Ghardaia BP.511, 30 000, Ouargla, Algeria
;
2
Ecole Normale Supérieure de Ouargla, Ouargla, Algeria
Keyword(s):
Text Segmentation, Text Classification, Online Social Network, YouTube, Cooking, Named Entity Detection, Sentiment Analysis.
Abstract:
YouTube is one of the most used online social networking (OSN) websites for exchanging recipes. It allows uploading them, searching for, downloading, as well as rating and reviewing them. Sentiment analysis for food and cooking recipes comments is to identify what people think about such cooking recipe video through users’ comments. Nowadays, users’ give their opinion not only about recipes; they also evaluate the cook through their comments, where a cook’s reputation can affect the users’ opinion about his cooking recipes. Frequently, when a cook has a good reputation, his recipes receive a great success by people, and vice versa. In this paper, we propose a new approach that deal with the sentiment classification of cooking reviews. Firstly, we examine the benefit of performing named entity detection and conjunctions on our corpus for text segmentation in order to divide the comment on segments concerning the cook and segments concerning the recipe. Next, we make two sentiment clas
sifications (about the cook and about the recipe). Finally, we incorporate the polarity of the cook sentiment classification in the recipe sentiment classification in order to analyse the effect of the opinion about the cook on the performance of the categorization of the shared cooking recipes comments in OSNs.
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