Natural Language Processing: An Analysis of Sentiment.

Natural Language Processing: An Analysis of Sentiment.

In this project I will investigate the basics of Natural Language Processing (NLP) and aim to predict whether a sample of tweets are either positive or negative. This will consist of combining machine learning principles with text.

In this project I will investigate the basics of Natural Language Processing (NLP) and aim to predict whether a sample of tweets are either positive or negative. This will consist of combining machine learning principles with text. I will use mathematics and statistics to get the text in a format that algorithms can understand.

Understanding the problem statement and business case

  • Natural language processors (NLP) works by converting words (text) into numbers
  • These numbers are used to train an AI/ML models to make predictions
  • Predictions could be sentiment inferred from social media posts and product reviews
  • AI/ML-based sentiment analysis is crucial for organisations to automatically predict whether their customers are happy or not
  • The process could be done automatically without having humans manually review thousands of tweets and customer reviews
  • In this case study, we will analyse Twitter tweets to predict people’s sentiment

For instance:

TWEET: “Good morning everyone! Such a beautiful day!!” -> SENTIMENT ANALYSIS (NLP MODEL) -> SENTIMENT: POSITIVE (Label 0)

TWEET: “The food was awful and the waiters rude.” -> SENTIMENT ANALYSIS (NLP MODEL) -> SENTIMENT: NEGATIVE (Label 1)

The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a derogatory or negative sentiment associated with it.

Content

Full tweet texts are provided with their labels for training data.

Mentioned users’ username have been redacted and replaced with @user. The user column will be dropped.

1,600,000 tweets extracted using the twitter api . The tweets have been annotated (0 = negative, 4 = positive) and they can be used to detect sentiment .

Data can be obtained from this kaggle dataset: https://www.kaggle.com/kazanova/sentiment140

twitter nlp sentiment-analysis machine-learning data-science

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