Building Sentiment Predictor - Lexicon Based (2)

Building a Sentiment Analysis Predictive Model - Lexicon Based Approach

This workflow uses a Kaggle Dataset including thousands of customer social media posts towards six US airlines. Contributors annotated the valence of the tweets as positive, negative and neutral. In the lexicon based approach, the number of words with a positive and a negative meaning are counted per post. Based on these numbers, a sentiment score is calculated and used to classify the posts.

1. Read annotated dataset.

Besides the node to read CSV files below, KNIME provides a wide range of nodes to read different datastet formats (e.g., parquet, json, images etc.).

2. Data Manipulation/Preparation.

Here the most important node is String to Document, which formats sevaral string columns (e.g., author, text, title) into a single document that can be text-mined in KNIME.

3. Use Text Mining to Tag Words with Positive and Negative Meaning based on a Dictionary.

6. Evaluate the Prediction.

4. Count the Number of Positive and Negative Words per Document.

5. Calculate a Sentiment Score based on the Number of Positive and Negative Words and Classify Documents based on the Score.

The sentiment score is calculated by (number of postive words - number of negative words) divided by (number of postive words + number of negative words).

If the score is negative, the post is classified as negative; if the score is positive, it is classified as positive; and if it is equal to 0, the post is classified as neutral.