Mixing social media and AI to help humanity


What is social media NLP?

Social media NLP for smart city traffic management

At the core of it, the problem is to analyze the tweets and extract important heuristics to understand the traffic scenario of an area. There can be lots of ways to look into the technicality of this problem. One common way of dealing with the problem from a classification perspective is to train a classification model to classify the tweets into one of the pre-defined classes, where each class represents a traffic scenario. The classification simply indicates what type of traffic congestion the tweet is talking about. Researchers have also used neural networks, and deep learning techniques to perform the classification with a good deal of success. From a sentiment analysis perspective, there are also very useful works, where the researchers have analyzed the sentiment of the tweets, and then used the sentiment information to extract interesting heuristics about the current traffic scenarios.

These heuristics collected from the Twitter data can be fed into smart algorithms which can be used to re-route or manage traffic flows at congested areas of the city. Modern research in these domains has shown that the time spent by the vehicles in congestion can be significantly reduced using twitter-NLP backed smart algorithms.

Social media NLP for social monitoring.

Hate speech detection is a very useful domain of research that is useful to monitor social media for hateful content. hateful contents are very dangerous in a social media setting, and these models are useful to track them down and delete them. This can be represented as a classic case of sentiment analysis, where the 3 levels of sentiments are hateful, neutral, and non-hateful. Researchers have been very successful in identifying these hateful comments among social media texts.

However, there is more to this problem than just sentiment analysis. Take, for example, the tweets generated in India. A huge percentage of the population writes their tweets in their local language using the English alphabets. Some of the hateful sentiments are hidden in the form of sarcasm or past references that can be hard to detect by machine learning models. Researchers are implementing various techniques to detect these hidden hate-comments. One approach is to build a dictionary of the most common abuses in a given Indian language, written using the English alphabets. Datasets of hate-speech are also being built in different languages to support the cause. Hate Speech masquerading as sarcasm remains a huge area to explore.

Social media NLP for disaster management


Originally published at https://www.linkedin.com.



Graduate researcher@University of Hyderabad.

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