Social media emergence has gained significant impact on how people communicate and socialize. Twitter provides the social media platform from where opinions of the people can be heard. Sentimental analysis can be applied to obtain the useful information by analyzing these tweets carefully. To characterize the human opinion, this paper studies users perception regarding a controversial product, namely self-driven cars. To find people’s opinion regarding this new technology, self-driven car Twitter dataset is used. Based on the people’s reaction about the self driven car in the social media(Twitter), human opinions are characterized like whether the people gave positive statement, negative or neutral statement regarding the self-driven car tweets. To classify the tweets, different machine learning algorithms, such as Logistic regression, Support Vector Machine, Random forest classifier and AdaBoost classifier are used. By using these tweets, opinions are characterized as “positive”, “negative” and “neutral”. To evaluate the performance of four algorithms, comparisons is carried out over the metrics like accuracy, recall, precision and f1-score. From the experimental results Logistic regression outperforms Support Vector Machine, Random forest classifier and AdaBoost classifier algorithms.

Random forest, Support Vector Machine, Logistic Regression , AdaBoost classifier, Sentiment analysis

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