##Twitter Sentiment Analysis: Problem Statement In this project, we try to implement an NLP Twitter sentiment analysis model that helps to overcome the challenges of sentiment classification of tweets. We will be classifying the tweets into positive or negative sentiments. The necessary details regarding the dataset involving the Twitter sentiment analysis project are:
The dataset provided is the Sentiment140 Dataset which consists of 1,600,000 tweets that have been extracted using the Twitter API. The various columns present in this Twitter data are:
target: the polarity of the tweet (positive or negative) ids: Unique id of the tweet date: the date of the tweet flag: It refers to the query. If no such query exists, then it is NO QUERY. user: It refers to the name of the user that tweeted text: It refers to the text of the tweet
The various steps involved in the Machine Learning Pipeline are:
Import Necessary Dependencies Read and Load the Dataset Exploratory Data Analysis Data Visualization of Target Variables Data Preprocessing Splitting our data into Train and Test sets. Transforming Dataset using TF-IDF Vectorizer Function for Model Evaluation Model Building Model Evaluation
Twitter Sentimental Analysis is used to identify as well as classify the sentiments that are expressed in the text source. Logistic Regression, SVM, and Naive Bayes are some of the ML algorithms that can be used for Twitter Sentimental Analysis.