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Okay, so we have our tweets, but they're just a bunch of text, right? We need to figure out the sentiment behind them – are people feeling positive, negative, or neutral about the stock? This is where **sentiment analysis** comes to the rescue! Sentiment analysis is essentially the process of computationally identifying and categorizing opinions expressed in a piece of text. There are several ways to perform sentiment analysis. You can use pre-trained sentiment analysis models, like those available through the `TextBlob` library, which provides a simple way to access sentiment scores. TextBlob, along with other libraries, assigns a polarity score (ranging from -1 to 1) and a subjectivity score (ranging from 0 to 1) to the text. The polarity score indicates whether the sentiment is negative, positive, or neutral, while the subjectivity score indicates how subjective the text is. You can also use other techniques like **VADER (Valence Aware Dictionary and sEntiment Reasoner)**, which is specifically designed for analyzing sentiments in social media text. VADER is particularly good at understanding the nuances of language, including emoticons and slang.
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