This paper discusses the results obtained for different techniques applied for performing the sentiment analysis of social media (Twitter) code-mixed text written in Hinglish. The various stages involved in performing the sentiment analysis were data consolidation, data cleaning, data transformation and modelling. Various data cleaning techniques were applied, data was cleaned in five iterations and the results of experiments conducted were noted after each iteration. Data was transformed using count vectorizer, one hot vectorizer, tf-idf vectorizer, doc2vec, word2vec and fasttext embeddings. The models were created using various machine learning algorithms such as SVM, KNN, Decision Trees, Random Forests, Naive Bayes, Logistic Regression, and ensemble voting classifiers. The data was obtained from a task on Codalab competition website which was listed as Task:9 on the Semeval-2020 competition website. The models created were evaluated using the F1-score (macro). The best F1-score of 69.07 was achieved using ensemble voting classifier.

中文翻译：

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混合代码的社交媒体文本的情感分析（英语）

本文讨论了用于执行以Hinglish编写的社交媒体（Twitter）代码混合文本的情感分析的不同技术所获得的结果。进行情感分析的各个阶段包括数据整合，数据清理，数据转换和建模。应用了各种数据清理技术，在五次迭代中清理了数据，并在每次迭代后记录了进行的实验结果。使用计数矢量化程序，一种热矢量化程序，tf-idf矢量化程序，doc2vec，word2vec和快速文本嵌入对数据进行转换。这些模型是使用各种机器学习算法（例如SVM，KNN，决策树，随机森林，朴素贝叶斯，逻辑回归和整体投票分类器）创建的。数据来自Codalab竞赛网站上的一项任务，该任务在Semeval-2020竞赛网站上列为Task：9。使用F1分数（宏）评估创建的模型。使用整体投票分类器可达到69.07的最佳F1得分。