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Python Tutorial Sentiment analysis types and approaches
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http://youtube.com/watch?v=5CBDoqMswK0
Want to learn more? Take the full course at https://learn.datacamp.com/courses/se... your own pace. More than a video, you'll learn hands-on coding quickly apply skills to your daily work. • --- • Welcome back! In the previous video, we learned what sentiment analysis is and why it is useful. • But how do we even start with a sentiment analysis task? • Sentiment analysis tasks can be carried out at different levels of granularity. • First is document level. This is when we look at the whole review of a product, for example. • Second is the sentence level. This refers to determining whether the opinion expressed in each sentence is positive, negative, or neutral. • The last level of granularity is the aspect level. The aspect refers to expressing opinions about different features of a product. Imagine a sentence such as The camera in this phone is pretty good but the battery life is disappointing. It expresses both positive and negative opinions about a phone and we might want to be able to say which features of the product clients like and which they don't. • The algorithms used for sentiment analysis could be split into 2 main categories. • The first is rule or lexicon based. Such methods most commonly have a predefined list of words with a valance score. For example, nice could be +2, good +1, terrible -3, and so on. • The algorithm then matches the words from the lexicon to the words in the text and either sums or averages the scores in some way. • As an example, let's take the sentence, 'Today was a good day.' • Each word gets a score, and to get the total valance we sum the words. In this case, we have a positive sentence. • A second category is automated systems, which are based on machine learning. This is going to be our focus in this course. The task is usually modeled as a classification problem where using some historical data with known sentiment, we need to predict the sentiment of a new piece of text. • We can calculate the valance score of a text, using Python's textblob library. • We continue working with our 'Today was a good day' string. • We import the TextBlob function from the textblob package and apply it to our string. A TextBlob object is like a Python string, which has obtained some natural language processing skills. We can call different properties of the TextBlob object. We are interested in its sentiment; that's why we call sentiment on our TextBlob. • The sentiment property returns a tuple: polarity, which is measured on the scale from [-1.0 to 1.0], where -1.0 is very negative, 0 is neutral and +1.0 is very positive. Our example 'Today was a good day' carries positive emotion and thus will have a positive polarity score: 0.7. The second element in the tuple displays the subjectivity, measured from [0.0 to 1.0] where 0.0 is very objective and 1.0 is very subjective. So our example is rather positive and subjective. • Which method should one use? A machine learning sentiment analysis relies on having labeled historical data whereas lexicon-based methods rely on having manually created rules or dictionaries. • Lexicon-based methods fail at certain tasks because the polarity of words might change with the problem, which will not be reflected in a predefined dictionary. • However, lexicon-based approaches can be quite fast, whereas Machine learning models might take a while to train. At the same time, machine learning models can be quite powerful. • So, the jury is still out on that one. Many people find that a hybrid approach tends to work best in many, usually complex scenarios. • Now let's test what we've learned by solving some exercises! • #PythonTutorial #DataCamp #Sentiment #Analysis #Python
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