Sentiment analysis is a natural language processing (NLP) technique used to identify the sentiment or opinion expressed in a piece of text. Gensim is a popular Python library for topic modeling, document similarity analysis, and now it also supports sentiment analysis. In this blog post, we will explore how to perform sentiment analysis using Gensim in Python.

Installing Gensim

Before we get started, make sure you have Gensim installed. You can install it using pip:

pip install gensim

Getting the Data

To perform sentiment analysis, we need a dataset containing labeled text data. There are various datasets available online for sentiment analysis, such as the Sentiment140 dataset. You can choose any dataset that suits your requirements or use your own labeled dataset.

Preprocessing the Data

Once you have the dataset, the next step is to preprocess the data before feeding it to the sentiment analysis model. Preprocessing involves removing any unnecessary characters, converting the text to lowercase, removing stopwords, and performing tokenization.

Let’s take a look at an example of how to preprocess the data using Gensim:

from gensim.utils import simple_preprocess
from gensim.parsing.preprocessing import STOPWORDS

def preprocess(text):
    # Tokenize the text
    tokens = simple_preprocess(text)
  
    # Remove stopwords
    tokens = [token for token in tokens if token not in STOPWORDS]
  
    return tokens

Building the Sentiment Analysis Model

Gensim provides a built-in wrapper for the fastText library, which is a popular library for efficient text classification. To build the sentiment analysis model, we can utilize this wrapper.

from gensim.models.fasttext import FastText

# Train the sentiment analysis model
model = FastText(size=100, window=3, min_count=1, workers=4)
model.build_vocab(sentences=data)
model.train(sentences=data, total_examples=len(data), epochs=10)

In the above code, we create a FastText model with a vector size of 100, a window size of 3, and a minimum count of 1. We then build the vocabulary and train the model using the preprocessed data.

Using the Sentiment Analysis Model

Now that we have trained our sentiment analysis model, we can use it to predict the sentiment of new text data. We simply pass the text through the model and get the predicted sentiment.

def predict_sentiment(text):
    tokens = preprocess(text)
    sentiment = model.predict(tokens)
    return sentiment

Conclusion

In this blog post, we have explored how to perform sentiment analysis using Gensim in Python. We covered the installation of Gensim, preprocessing the data, building the sentiment analysis model, and using the model to predict sentiment. Gensim provides a powerful and efficient way to perform sentiment analysis on text data, making it an excellent choice for NLP tasks.