Financial Sentiment Analysis 📈: Project Overview

Created a model that can classify a Financial sentence as a Positive, Negative, or Neutral sentiment with (66% Accuracy) to detect polarity within the text.

Pulled over 5842 examples from Kaggle using pandas and opendatasets libraries in python.

Applied Logistic Regression, Support Vector Classifier, Random Forest Classifier, Bernoulli Naive Bayes, and KNeighborsClassifier and optimized using GridSearchCV to find the best model.

Code Used

Python version: Python 3.7.11

Packages: pandas, opendatasets, seaborn, matplotlib, numpy, nltk, wordcloud, collections, imblearn.over_sampling, re, string and textblob

For Web Framework Requirements: pip install -r requirements.txt

Resources Used

The dataset from Kaggle

How to download Kaggle datasets to Jupyter notebook guide

Cheatsheet for Markdown

Data Collection

Used Kaggle to pull the datasets 5842 books with 2 columns:

• Sentence
• Sentiment

Data Cleaning

After pulling the data, I cleaned up the dataset to reduce noises in the dataset. The changes were made follows:

• Made lowercase the sentences, cleaned punctuations in the sentences, deleted the newlines, removed numbers and possible links from the sentences.
• Removed stop words from the sentences and lemmatized them.

Exploratory Data Analysis

Visualized the cleaned data to see the trends.

• Created WordCloud for Sentence variables.

• Created Donut chart for Sentiment data. It looks like our data contains negative sentiments more than half of the whole dataset.

• Created 2-Gram Analysis Bar Graphs for Sentence variables.

• Created a histogram for Polarity Score in Sentences Sentences with negative polarity will be in range of [-1, 0), neutral ones will be 0.0, and positive reviews will have the range of (0, 1).

• Created a histogram for Length of Sentences Based on this histogram, we know that our review has text length between approximately 50-100 characters.

• Created a histogram for Word Counts in Sentences From the figure above, we infer that most of the reviews consist of 5-15 words.

Model Building

Encoded the target variable:

• Sentiment variables were encoded.

Gave importance of each words in the Sentence column with Term Frequency - Inverse Document Frequency (TF-IDF) Vectorizer.

Resampled the dataset with Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset.

Data were split into train (80%) and test (20%) sets.

I used six models (Decision Tree Classifier, Logistic Regression, Support Vector Classifier, Random Forest Classifier, Bernoulli Bayes, and KNeighborsClassifier) to predict the sentiment and evaluated them by using Accuracy.

Model Performance Evalution

Logistic Regression model performed better than any other models in this project.

Model	Test Accuracy Score
Decision Tree	0.5323477929984781
Logistic Regression	0.6259826132771339
Support Vector Classifier	0.5890109471958787
Random Forest Classifier	0.5638441049057488
Naive Bayes	0.5997986184287554
K-Neighbots	0.5013675213675214

Hyperparameter Tuning

We got the best accuracy 65.08 % with GridSearchCV and find the optimal hyperparameters.

Best Model

Applied Logistic Regression model with the optimal hyperparameters and got 66% Accuracy score.

Confusion Matrix

The Confusion Matrix above shows that our model needs to be improved to categorize sentiments better.

Thanks for reading :)