"Sentiment Analysis" Problem Statement Twitter has now become a useful way to build one's business as it helps in giving the brand a voice and a personality. The platform is also a quick, easy and inexpensive way to gain valuable insight from the desired audience. Identifying the sentiments about the product/brand can help the business take better actions. You have with you evaluated tweets about multiple brands. The evaluators(random audience) were asked if the tweet expressed positive, negative, or no emotion towards a product/brand and labelled accordingly. Dataset Description This dataset contains around 7k tweet text with the sentiment label. The file train.csv has 3 columns tweet_id - Unique id for tweets. tweet - Tweet about the brand/product sentiment - 0: Negative, 1: Neutral, 2: Positive, 3: Can't Tell Evaluation Metric We will be using ‘weighted’ F1-measure as the evaluation metric for this competition. For more information on the F1-metric refer to https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html