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Sentiment Analysis Project Report.pptx
- 2. Rationale behind the project • Using computers to perceive human emotions • Understand how words can reveal sentiments • Discover interesting books from reviews • Gain deeper understanding of GRU
- 3. Ethical Considerations made during the implementation • Eliminating user identifiable attributes • Minimizing bias • Dealing with profane words • Labels of the sentiments
- 4. Accuracy of the Output • Training set (0.9944) versus validation set accuracy (0.6275) • Model able to generally classify review’s sentiments • Few exceptions of wrong classification • Constructive criticism labelled negative • Sarcastic remarks labelled positive
- 5. Implementation Details • Used Google Colab • Dataset loaded using wget • XML file parsed using beautiful soup library & regular expressions • Negative and positive book reviews selected to train model
- 6. Implementation Details • Truncating sentences • Lemmatization • Removing stop words • Lowercasing • Converting reviews to integer sequences
- 7. Implementation • Training validation split • Using GRUs • 5 epochs • Testing the model by inputting own reviews
Editor's Notes
- I preferred NLP project that involved training a computer to predict the sentiments of people. How computers can perceive human emotions has always been a subject of fascination to me. I also have deep interests in linguistics and wanted to explore how words reveal the emotions of people. I was also hoping to find some interesting books to read by going over the reviews of different customers. Most importantly, I wanted to understand how I could Deep Neural Networks such as Gated Recurrent Units work to process complex information and train models that can make accurate and reliable predictions.
- The dataset contained a lot of user identifiable information such as their name, location and email address. I had to eliminate all those attributes and remain with the review texts only and their corresponding sentimental labels. I also eliminated the other attributes to minimize bias by avoiding stereotyping users according to reviews provided by the dataset. Bias could take place, for example making the model learn that women might have more negative sentiments compared to men. Another issue was dealing with profane words that might have been used in the reviews to the data. Additionally, the label of the sentiments seemed extreme to me. I though happy or sad could have been better than negative or positive.
- The model had a very high accuracy score of 99.44% on the training set and lower accuracy of 62.75% of the validation set. This implied that there was a problem of overfitting. However, the model was able to generally classify the emotions present in the reviews as negative or positive accurately. However, there were some few exceptions of wrong classifications. For instance, constructive criticism towards a book was labelled negative while sarcastic remarks were labelled positive. In my opinion, some of the classifications of the review
- The implementation was performed using Google Colab since training the model required high computation resources such as GPUs and TPUs. The dataset was loaded to Google Colab using the wget bash command. Since the original data was an XML file, it was parsed using beautiful soup library and regular expresso. The negative and positive book reviews were selected to be used to train the model/
- The data preprocessing stage involved truncating the reviews of customers to remain with the first 150 characters. Truncating could make training the model to take less time and be more effective. It was presumed that a customer’s sentiments towards a product can be detected while using the last few sentences. Lemmatization involved converting the words into their base form to derive meaning. Removing stop words such as and, the, I, and we was necessary to reduce the number of vocabularies that did not carry much meaning. Lowercasing enabled the text to remain uniform. Finally, the vocabularies of the reviews were transformed into integer indexes enabling conversion of the reviews to integer sequences. This step was necessary to make it easier for the machine learning model to learn from the data.
- Before training the model, the data was split into training and validation sets in the ratio of 80:20. This was necessary to track the performance of the model during training. Gated Recurrent Units were selected because they are one of the most advanced forms of Recurrent Neural Networks which determine which information is to be forgotten and which is to carried on to the next stage of processing. The model was trained using 5 epochs. The small number of epochs was selected to prevent the model from overfitting. The model was tested using user input where users could write their own reviews and the model would predict whether it was positive or negative.