Sentimental Analysis of Textual Data

The objective of this project is to conduct a comparative study of sentimental analysis of textual data using machine learning techniques, specifically employing an Early Classification Based Approach for Fault Classification. The emotions under consideration are sadness, joy, love, anger, fear, and ego, which are commonly expressed in text data. To achieve this goal, three machine learning algorithms, namely Stochastic Decision Tree (DT), Random Forest (RF), and Long Short-Term Memory (LSTM), are utilized. The study utilizes a dataset containing diverse text samples expressing different emotions, which is pre-processed to remove irrelevant information and then split into training and testing sets. The DT, RF, and LSTM models are trained on the training set and evaluated on the testing set using various evaluation metrics. The experimental results provide insights into the performance of the three algorithms for sentimental analysis through text. The findings reveal that DT and RF achieve comparable accuracy levels, while LSTM outperforms them in terms of overall classification accuracy However, Random Forest exhibits better performance in capturing sequential patterns and contextual information in text data, making it particularly effective for emotions expressed through longer and more complex text samples, such as ego. On the other hand, DT and RF may perform better for emotions expressed through shorter and more straightforward text samples, such as joy and sadness, due to their relative simplicity. This research contributes to the field of emotion classification and provides valuable insights for researchers and practitioners in selecting appropriate machine learning algorithms for emotion classification tasks. The early classification-based approach for fault classification in the context of emotion classification from text opens up avenues for further research in this area.