There is a lot of information available in the text format on the web. We can understand the sentiments about websites/products/brands when we read. Here we will teach the computer to do the hard work of reading and understanding all of the reviews on Amazon for us. The project's objective is to extract, manipulate and create a good deep learning model to understand the users' sentiments classifying texts sent by a human being into Positive, Neutral, and Negative.
- Created a Sentiment Analysis with Deep Learning to recognize sentiment in a text.
- Scraped over 3000 reviews for Google Play Store and App Store using APIs
- Built a pipeline that text preprocess the reviews and brings them to a predictable and analyzable form for the neural network.
- Converted text inputs into embedded vectors using Global Vectors For Word (GloVe)
- Built a sentiment analysis using Keras modules Model, Dense, LSTM, and Embedding.
Python Version: 3.7
Packages: pandas, numpy, google_play_scraper, app_store_scraper, re, unidecode, nltk, matplotlib, plotly, wordcloud, imblearn, sklearn, tensorflow.keras
To build a pipeline for text preprocessing, I built a few functions to do the following steps:
- Lowercase the text and remove HTML
- Remove accented characters
- Extended contractions
- Remove special characters
- Lemmatization
- Remove stop words
- Check if it is an English word and remove digits and duplicate white spaces.
For the pipeline, I define three classes to apply all the text preprocessing previous functions on the reviews. The first class will apply all the functions created to prepare the text into a regular expression. The second class will remove all the stop words, and the last class will lemmatize the remnant terms of the previous processes.
For a good understanding of the Amazon reviews, I plotted the text differently.
- Using pie graph I plotted the distribution
- The Word Cloud
- Lastly I plotted the n-gram. One bar chart for 1 grams, 2 grams, 3 grams and 4 grams of each sentiment.
Before I built the deep learning model, I went through a few steps. I split the data frame into Train and Test. Second, I vectorized and embedded the reviews, and to prevent overfitting, I balanced the Train set and reduced the number of features. Next, I created three functions of metrics that will use in the model.
I built a Deep Learning model with one input layer shape 20. One Embedding Layer takes the vocab size and the previous embedding matrix. One Short-Term Long Memory (LSTM) layer with 128 neurons and finely the output layer with 3 neurons since we have 3 labels in the output, Positive, Neutral, and Negative. So, that's the final model.
The training and validation shows very good results and are both always close to each other. Also the model keeps improving each epoch. When I evaluated in the testing df I reached the following scores:
- Loss: 0.600444
- Accuracy: 0.742218
- Precision: 0.754194
- Recall: 0.731821
- F1-Score: 0.742558
For the final test, I build a function that applies our pipeline for text preprocessing and classifies a comment with our model, and returns if the comment is Positive, Negative, or Neutral.
