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Abstract: In order for sentiment classifier training for sentiment classifier training for numerous tweets at once, we suggest a collaborative multi-Trends sentiment classification strategy. When labelled data is scarce, our method uses the sentiment data from several tweets to train more accurate and reliable sentiment classifiers for each Trend. In particular, we split each Trend's sentiment classifier into two parts: a general one and a Trends-specific one. There are currently a lot of consumer reviews of many topics available online. automatically picks up the key details from online customer reviews. Two observations are used to determine the key features of the product. with the intention of early trend classification. This would make it possible to offer end users a filtered subset of trends. Based on the social diffusion of trends, we analyse and test a set of simple language-independent criteria to classify them into the proposed typology. We investigate two different types of Trending similarity measures, one based on textual content and the other on sentiment expressions. Also, we present two effective strategies to resolve the model of our method. The performance of multi-Trends sentiment classification can be effectively improved by utilizing our methodology, which too significantly outperforms standard techniques, according to experimental results on benchmark datasets.