Posted on

How To Perform Sentiment Analysis in Python 3 Using the Natural Language Toolkit NLTK

Getting Started with Sentiment Analysis using Python

nlp for sentiment analysis

However, before cleaning the tweets, let’s divide our dataset into feature and label sets. Defining what we mean by neutral is another challenge to tackle in order to perform accurate sentiment analysis. As in all classification problems, defining your categories -and, in this case, the neutral tag- is one of the most important parts of the problem.

Sentiment analysis models can help you immediately identify these kinds of situations, so you can take action right away. Once you’re familiar with the basics, get started with easy-to-use sentiment analysis tools that are ready to use right off the bat. In this step, you converted the cleaned tokens to a dictionary form, randomly shuffled the dataset, and split it into training and testing data. The most basic form of analysis on textual data is to take out the word frequency. A single tweet is too small of an entity to find out the distribution of words, hence, the analysis of the frequency of words would be done on all positive tweets.

Sentiment analysis refers to analyzing an opinion or feelings about something using data like text or images, regarding almost anything. For instance, if public sentiment towards a product is not so good, a company may try to modify the product or stop the production altogether in order to avoid any losses. This is the fifth article in the series of articles on NLP for Python. In my previous article, I explained how Python’s spaCy library can be used to perform parts of speech tagging and named entity recognition.