Main Article Content
The sentimental or opinion details in web pages rises in parallel with the development of web 2.0. Capturing such sentimental information from the web 2.0, is a challenging task. The problem is addressed in this paper by introducing a new paradigm for implementing Long Short-Term Memory Networks. Word2Vec Model is used to transform sentimental contents into input embedding vectors. These input embedding vectors are then fed into the LSTM, which predicts the content's sentiment details. The suggested approach is put to the test on two well-known datasets: IMDB and Amazon Analysis. In the IMDB dataset, the suggested approach achieves an accuracy of 0.87, and in the Amazon analysis dataset, it achieves an accuracy of 0.89.