Sentiment Analysis Data Preprocessing in Python: Stop Words Removal & Word2Vec Embeddings

This Python code focuses on data preprocessing for sentiment analysis, preparing text data for a machine learning model. It involves steps like removing stop words and creating word2vec embeddings, but does not utilize Jieba for Chinese text segmentation. Instead, it uses regular expressions to remove unwanted characters and relies on a custom stop word list to filter out common words. Here's a breakdown of the code's functionality:

1. Data Preparation:

• data_preview(file_path): Previews the original data set, displaying its size, descriptive information, and a sample of the data in a DataFrame format.
• stopwordslist(): Creates a list of stop words by reading a stop word file specified in the Config object.

2. Word2ID Mapping:

• build_word2id(file): Constructs a dictionary (word2id) that maps words to unique IDs, considering stop words and removing short words. This mapping is essential for representing text as numerical data for machine learning.
• build_id2word(word2id): Creates a reverse mapping from ID to word, allowing you to recover the original words from numerical representations.

3. Word2Vec Embeddings:

• build_word2vec(fname, word2id, save_to_path=None): Generates word2vec embeddings for the words in your data set. This step leverages pre-trained word2vec models (fname) and creates embeddings for those words found in your dataset. You can save the resulting embeddings to a file (save_to_path).

4. Text to Index Array Conversion:

• text_to_array(word2id, seq_length, path): Converts text data into numerical index arrays. It maps each word in the text to its corresponding ID from word2id. The seq_length parameter ensures a consistent length for all sequences. This function also handles the case where words are not found in the vocabulary by assigning them an ID of 0 (typically representing a padding token).
• text_to_array_nolabel(word2id, seq_length, path): Similar to text_to_array, but processes text data without labels.

5. Categorical Encoding of Labels:

• to_categorical(y, num_classes=None): Converts label data into one-hot encoding, suitable for use in machine learning models. It transforms labels into a binary representation, where each class corresponds to a unique position with a '1' and the rest with '0'.

6. Data Preparation for Training:

• prepare_data(w2id, train_path, val_path, test_path, seq_lenth): Prepares training, validation, and testing data by converting the text and labels into numerical index arrays. This function relies on the previous text-to-array conversion and one-hot encoding functions.

7. Example Usage:

The code includes an example usage section that showcases how to apply the preprocessing steps. It previews the data, builds mappings, creates word2vec embeddings, prepares data for training, and saves the processed data to files.

Important Note: While this code effectively prepares data for sentiment analysis, it does not include Chinese text segmentation (using Jieba). If you're working with Chinese text, you would need to incorporate Jieba into your preprocessing pipeline for accurate word tokenization.

Further Considerations:

• Jieba Integration: If your data is in Chinese, consider integrating Jieba to perform segmentation and improve your results. You can adapt the code to include Jieba's functionality.
• Data Augmentation: Exploring data augmentation techniques can improve model performance, especially with limited data. You can look into methods like synonym replacement or back-translation.
• Experiment with Different Preprocessing Steps: Try adjusting the stop words, sequence length, and other parameters to see how they impact model performance. You can fine-tune your preprocessing pipeline to optimize for your specific task and dataset.