By Huan Liu, Shamanth Kumar, Fred Morstatter
This short presents tools for harnessing Twitter information to find strategies to advanced inquiries. The short introduces the method of accumulating info via Twitter’s APIs and gives innovations for curating huge datasets. The textual content provides examples of Twitter info with real-world examples, the current demanding situations and complexities of creating visible analytic instruments, and the simplest thoughts to deal with those matters. Examples reveal how strong measures could be computed utilizing a variety of Twitter information resources. as a result of its openness in sharing facts, Twitter is a first-rate instance of social media within which researchers can determine their hypotheses, and practitioners can mine fascinating styles and construct their very own functions. This short is designed to supply researchers, practitioners, venture managers, in addition to graduate scholars with an access element to leap begin their Twitter endeavors. It additionally serves as a handy reference for readers professional in Twitter info research.
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Example text
While you can create as many indexes as you want for a given collection, you can only use one for each query. If you have multiple fields in your query, you can create a “compound index” on both fields. For example, if you want to create an index on “timestamp”, and then “retweet_count”, can pass {"timestamp": 1, "retweet_count": 1}. 9 Filtering Documents: Number of Tweets Generated in a Certain Hour 29 • Indexes can only use fields in the order they were created. Say, for example, we create the index {"timestamp": 1, "retweet_count": 1, "keywords" : 1}.
John’s Tweet is compared against a lexicon of words and their likelihood to be positive/negative. The most probable label is then taken as that Tweet’s sentiment or “negative”. After looking at these words, the algorithm then judges whether the text in the Tweet is positive or negative based on the likelihood for each possibility. A workflow is shown in Fig. 6. 4 When choosing a sentiment lexicon, we need to be careful about the source used to build it. Words have different sentiments in different contexts.
The sentiment analysis algorithm we use in this book is based on a Naïve Bayes Classifier. It classifies a Tweet as positive or negative by comparing each word in the Tweet with the labeled words in the lexicon. If the words in the Tweet have been used more in positive Tweets, then the Tweet is labeled as positive. On the other hand, if the words in the Tweet have been associated more with negative Tweets, then the Tweet is labeled as negative. 2 Building a Lexicon Automatically To get around the potential issue of having an unsuitable lexicon, we will construct our lexicon automatically for each dataset.