One of the common tasks in natural language processing is sentiment analysis. If you want to work on a project based on natural language processing, sentiment analysis will be a good choice. Before you start a project on sentiment analysis, you must be confused about which Python library you should choose. So, if you want to learn about the best Python libraries you should choose for sentiment analysis, this article is for you. In this article, I’ll walk you through the best Python libraries for sentiment analysis and which one you should choose.
Best Python Libraries for Sentiment Analysis
NLTK:
NLTK is one of the best Python libraries for any task based on natural language processing. Some of the applications where NLTK is best to use are:
- Sentiment Analysis
- Named Entity Recognition
- Part of Speech Tagging
- Topic Modelling
The SentimentIntensityAnalyzer function of this library is very useful for the task of analyzing sentiments in a few lines of code. When I work on any task based on sentiment analysis, NLTK is always my first choice.
TextBlob:
TextBlob is another great Python library for any natural language processing task. Some of the applications where TextBlob can be used are:
- Sentiment Analysis
- Spelling Correction
- Noun Phrase Extraction
- Text Classification
The TextBlob library also has the SentimentIntensityAnalyzer function which works the same as in NLTK. So you can also use TextBlob instead of NLTK for sentiment analysis, but I still prefer to use Textblob for the tasks based on spelling correction.
SpaCy:
SpaCy is an industry-standard library that provides extensive functionality for natural language processing applications. Some of the applications where SpaCy can be used are:
- Sentiment Analysis
- Named Entity Recognition
- Model Packaging and deployment
- Part of speech tagging
Although Spacy claims this is an industry force for natural language processing in Python, I will still prefer TextBlob and NLTK for sentiment analysis. Without a doubt, this is one of the best libraries for sentiment analysis, but I would still prefer SpaCy for named entity recognition only.
Summary
So these were some of the best Python libraries you can choose for sentiment analysis tasks. NLTK and TextBlob provide the same functionality for sentiment analysis where SpaCy can be used differently. If I have to choose a library for sentiment analysis, I will prefer NLTK. As mentioned above, I will prefer TextBlob for spelling correction and SpaCy for named entity recognition, as NLTK will be my first choice for all the natural language processing tasks. Hope you liked this article on the best Python libraries for sentiment analysis. Please feel free to ask your valuable questions in the comments section below.
