Aspect-based sentiment analysis on Twitter social network data about vegetarianism

Vegetarianism is one of the trends that has received a lot of feedback on social networks. The content published by users reflects their feelings and opinions towards this trend and its various aspects. In this regard, a dataset containing more than sixty thousand tweets published in 2023 about vegetarianism was collected. This dataset was used to extract user sentiment towards different aspects of vegetarianism. First, a method based on RoBERTa language model was proposed to analyze the implicit sentiment hidden in tweets. Then, using the Latent Dirichlet Allocation topic modeling approach, some relevant aspects and topics related to vegetarianism were extracted. In the next step, a method based on DeBERTa language model was used to analyze tweet sentiment towards different aspects that had been extracted. Various frequency and sentiment distribution charts for different aspects in the field of vegetarianism were examined. The results of emotional analysis based on RoBERTa and DeBERTa models were compared side by side. Data analysis using the DeBERTa model showed that users had mostly tweeted positive sentiments regarding the plant and lifestyle aspects. However, for the Animal aspect, most tweets were negative. For both Diet and Company aspects, most tweets were positive or neutral with values close to each other. During the discussion, some implicit knowledge related to this topic was also examined.