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As a result of the technological advancements of the internet, Bangladeshi users are increasingly active on social networks. In this sense, social media influencers are becoming more well-known and attracting a growing number of users. Bangladeshi food review influencers are becoming more and more well-known every day. The most sophisticated Bengali sequence classification model was used in this study's analysis of social network interaction data. Through an extensive exploration of the social media landscape, we delve into the realm of food reviews. We used the sequence classification model to classify the comments collected from social media for our study. Our findings reveal that the majority of viewers hold a positive perception of Bengali food reviews on social media, while a small number of outliers may express contrasting opinions. Notably, our classifier, BanglaBERT, achieves an impressive prediction accuracy of 83.76%, emphasizing the reliability and effectiveness of our approach.

随着互联网技术的迭代发展，孟加拉国用户在社交网络中的活跃度持续提升。在此背景下，社交媒体影响者（social media influencers）的知名度不断攀升，吸引的用户群体也日益壮大。其中，孟加拉国美食测评类社交媒体影响者的知名度正与日俱增。
本研究在分析社交网络交互数据时，采用了当前性能最优的孟加拉语序列分类模型。通过全面调研社交媒体生态，我们深入探索了美食测评领域，并借助该序列分类模型对从社交媒体采集的评论数据开展分类任务。
研究结果显示，绝大多数受众对社交媒体上的孟加拉语美食测评内容持正面评价，仅少数异类评论表达了相悖观点。值得注意的是，本研究使用的分类器BanglaBERT取得了83.76%的出色预测准确率，充分彰显了本研究方法的可靠性与有效性。