Emotion AI: How to Build Sentiment Prediction Models Like a Pro

Author(s): Shanaka C. DeSoysa

Originally published on Towards AI.

Master Sentiment Prediction: Techniques, Tips, and Tools for Every Data Scientist

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In this article, we’ll explore how to build sentiment prediction models using different machine learning techniques. We’ll cover traditional machine learning models like Logistic Regression, Naive Bayes, and Support Vector Machines (SVM), as well as advanced models like BERT, LSTM, RNN, and XGBoost with Word2Vec embeddings. We’ll also discuss the pros and cons of each approach and how to interpret the results.

Let’s start with a small sample dataset of employee exit survey comments:

data = [ {"text": "I loved working here, but I need to move to a new city.", "sentiment": "positive"}, {"text": "The work environment was toxic and stressful.", "sentiment": "negative"}, {"text": "It was an okay experience, nothing special.", "sentiment": "neutral"}, {"text": "I am unsure about my feelings towards this job.", "sentiment": "ambiguous"}]

TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure used to evaluate the importance of a word in a document relative to a collection of documents.

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.feature_extraction.text import TfidfVectorizer# Convert data to DataFramedf = pd.DataFrame(data)# Encode labelslabel_mapping = {'positive': 0, 'negative': 1, 'neutral': 2, 'ambiguous': 3}df['label'] = df['sentiment'].map(label_mapping)# Split dataX_train, X_val, y_train, y_val = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)# Vectorize text… Read the full blog for free on Medium.

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Published via Towards AI