Introduction
Briefly introduce the project and its objectives. Mention the significance of nderstanding sentiments in financial news headlines for retail investors.
Project Requirements:
Description of Data:
This dataset (FinancialPhraseBank) contains the sentiments for financial news headlines from the perspective of a retail investor. The dataset contains two columns, "Sentiment" and "News Headline". The sentiment can be negative, neutral or positive.
Task :
Perform EDA and necessary pre-processing steps in the dataset.
Using the LDA algorithm create the Topics (Min 10) for the Corpus
NOTE: Use News Headline column
Compute the coherence score and print Topics Extracted.
Visualize the topics
Plot the dependency parser for any two random sentences from the entire corpus/dataset that has at least 10 words in the sentence. Make sure that dependency parser looks good and should be visually understandable.
Solution Approach:
1. Dataset Used:
The dataset used in this project consists of headlines and their associated sentiments.
It contains a total of 4846 records.
2. Basic Data Information:
The dataset is loaded into a Pandas DataFrame to analyze its structure.
It comprises two columns:
Sentiment: Indicates the sentiment associated with each headline (neutral, negative, or positive).
News Headline: Contains the text of the headline.
3. Data Processing Techniques:
We perform various data processing steps to prepare the text data for analysis.
Tokenization using NLTK's word_tokenize.
Removal of stopwords to filter out common words that do not carry significant meaning.
Cleaning the text data by removing non-alphabetic characters.
4. Feature Selection:
We focus on the 'News Headline' column as our main feature for analysis.
WordCloud visualization is utilized to understand the most frequent words in different sentiment categories.
5. Testing and Training:
We split the dataset into subsets for testing and training purposes.
Exploratory data analysis techniques are employed to understand the distribution of sentiments in the dataset.
6. Algorithms Used:
We implement Latent Dirichlet Allocation (LDA), a topic modeling algorithm, to identify topics within the text data.
LDA model is trained on the preprocessed corpus to extract topics and their associated keywords.
7. Evaluation Used:
Coherence Score is calculated to evaluate the coherence of topics generated by the LDA model.
A higher coherence score indicates better-defined topics.
8. Screenshot of Output:
Visualizations such as WordClouds for positive sentiments, topic distributions, and dependency parse trees are showcased.
We leverage tools like PyLDAvis for interactive visualization of topic modeling results.
Dependency parse trees are generated using spaCy's dependency parser to understand the syntactic structure of sentences.
Output Screenshots
WordCloud :
coherence Score
Our team at CodersArts is dedicated to assisting you in tackling your big data analytics project, centered around understanding sentiments in financial news headlines. With expertise in data processing and advanced analytics techniques, we ensure a comprehensive approach to meet your project requirements.
From performing exploratory data analysis (EDA) to implementing Latent Dirichlet Allocation (LDA) for topic modeling, our team guides you through each step of the process. We utilize tools like NLTK and spaCy for efficient text processing and visualization, ensuring a deep understanding of the dataset.
With a focus on delivering actionable insights, we provide thorough evaluations, including coherence score calculations and visualizations of topic distributions. Our commitment to excellence extends to comprehensive documentation review and problem-solving sessions, ensuring the success of your big data analytics initiative.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.