The NLP-based sentimental analysis project aims to predict market views and provide stock option recommendations. By analyzing tweets related to a specific company, the model identifies the sentiment (positive, negative, or neutral) associated with each tweet. This analysis helps gauge market perception and sentiment towards the company, enabling better decision-making for stock options.

The project utilizes NLP's Word2Vec method for vectorization. Word2Vec represents words as high-dimensional vectors that capture the semantic meaning and relationships between words. By applying Word2Vec, the textual data from tweets is transformed into numerical features, allowing machine learning algorithms to process and classify the sentiment of each tweet.

The dataset used in this project consists of a collection of tweets from various sources. Each tweet in the dataset is labeled with a sentiment category, such as positive, negative, or neutral. This labeled dataset serves as the basis for training and evaluating the sentiment analysis model.

Before training the model, the tweets undergo preprocessing steps to clean and normalize the text data. The preprocessing steps include:
Removing special characters, URLs, and hashtags.
Tokenizing the text into individual words.
Removing stop words.
Lemmatizing or stemming the words to their base form.
These preprocessing steps help in reducing noise and improving the accuracy of the sentiment analysis model.

The sentimental analysis model is trained using various machine learning algorithms, including Naive Bayes, Random Forest, and KNN. These algorithms are applied to the labeled dataset, which consists of tweets and their corresponding sentiment labels. The model learns from this data to accurately classify new tweets into positive, negative, or neutral sentiment categories.