How do you collect, preprocess, and label sentiment data for training and testing sentiment models?

With the help of free API access from Twitter, Linkedin, and Facebook for academic purposes we can create datasets and save them in CSV. For the sentimental analysis, I leveraged Twitter's elevated API access along with the Tweepy library in Python. By obtaining the required API credentials and authenticating with Twitter, I utilized Tweepy's powerful functionalities to search for tweets relevant to my analysis. I specified parameters such as the search query, language, and tweet count, focusing on the last three months and the keyword "chatGPT" within North America as I want to understand the negativity of how chatGPT will swallow our jobs. After extracting the tweets, I saved the data in a CSV file.

Collecting sentiment data involves sourcing text from various platforms like social media, reviews, and surveys. Preprocessing entails cleaning text, removing noise like punctuation and stopwords, and tokenizing sentences. Labeling involves assigning sentiment labels (positive, negative, neutral) to each text snippet manually or using automated tools. This can be done through crowdsourcing platforms or by domain experts. It's crucial to ensure a balanced dataset to avoid bias and improve model performance. Finally, splitting the data into training, validation, and testing sets ensures robust model evaluation and performance optimization.

Accessing historical data for sentiment analysis is challenging, primarily due to limitations imposed by API providers. Researchers depend on data like posts and reviews from online sources, but often face restricted access. For example, the basic Twitter API only allows partial access to public data. This limitation hinders researchers from obtaining complete datasets, impacting the accuracy and effectiveness of their studies.

To refine the dataset, I employed the preprocessor, eliminating emojis, URLs, and retweets, while retaining only the plain text reflecting user sentiments about chatGPT. Following this data-cleaning step, I performed sentiment analysis using TextBlob, categorizing sentiments into five types extreme positive, mildly positive, neutral, mild negative and extreme negative to understand the polarity of tweets into more granular levels. I followed the following steps: -Loading NLTK and Setting Up Tools -Defining Regular Expression Patterns -Defining a Text Cleaning Function -Applying Cleaning Function to Tweets -Removing Rows with Empty Locations -Saving Cleaned Data to CSV

The data cleaning approach outlined is suitable for basic sentiment analysis methods like Naive Bayes with Bag of Words, but may not be ideal for advanced techniques. State-of-the-art sentiment analysis, particularly using neural networks and deep learning, effectively interprets natural language nuances. In these models, elements such as slang, punctuation, and emojis often have important sentiment implications. Therefore, over-cleaning or standardizing data in these advanced models could potentially remove valuable contextual clues, leading to less effective sentiment analysis. It's crucial to tailor the preprocessing steps to the specificities of the chosen model and the nature of the text being analyzed.

Sentiment analysis poses unique challenges in measuring inter-rater agreement due to its inherent subjectivity. This task requires annotators to discern and interpret sentiments and emotions from text, a process that naturally varies greatly among different individuals. Consequently, this subjectivity directly influences the levels of agreement achieved. In such a context, lower agreement scores should not be hastily interpreted as incorrect annotations.

I used Tableau to visualize the sentimental analysis dashboard. I used Line charts to show the timings when the polarity (sentiment) was extremely positive and when it was extremely negative. I followed the following steps: Data Preparation and Viz in Tableau: -Ensuring that the data fields, especially the sentiment scores and relevant categorical variables, are correctly recognized and formatted -Created various types of charts or graphs to represent sentiment distribution, trends, or comparisons (line charts for time series, donut chart for the share of polarity from the dataset, BAN for polarity percentage based on gender). -Map to visualize sentiment across different regions -Word Clouds for identifying the most used words.