Analysing Text – A better way

Words can be defined as one of the most effective ways of communication for people. The words can classify the emotions of a person and give them meaning. This classification can be hatred, happiness, sadness, anger, or other meanings. There are different applications for classifying the texts, such as sentiment analysis, detecting real or fake news, and organising the email into spam and non-spam. 

Text classification has been present in our lives for a long time and is used by different websites to block content such as Gmail to classify an email as spam, promotions, and social. 


When it comes to the internet, there has been an enormous increase in the number of users on the web. These users browse different websites to view other content.  The content available to us is hard to judge, whether real or fake.  Content has different motives such as marketing and advertising, news, or education and sadly negative impacts such as spreading hatred or violence.  This fake content causes false information among users, which eventually leads to certain disturbances in the community. For example, it was noted that 62 % of all United States adults received the news from social media in 2016. In contrast, in the year 2012, there was approximately 49 per cent acknowledged seeing information from social media [1]. 

It was also noted that social media now has a better reach than television as the source of news [2]. There are plenty of pros of social media, even though the authentication of the information circulated through social media is far less than other sources such as newspapers, news channels, radio, etc. Compared to other sources of news circulation, it is easier to circulate the news through social media to target a large audience. i.e., there are different purposes behind creating false information on the internet, ranging from a financial or political advantage. 

Artificial Intelligence in action 

Many techniques have been employed, and many are newly created to increase the precision of the text classification. Machine Learning, especially deep learning, a high research field nowadays, has contributed a lot to text classification and prediction using CNN (Convolution Neural Network), Bi-LSTM (Bi-direction Low Short-Term memory) and various other methods.   

Development from big companies like Google has also played an essential part in advancing this field. The launch of the attention mechanism by Google in the paper “Attention is all you need” [3] has also provided a base for developing different transformers models such as BERT, Al-BERT, ELECTRA, and others. These transformers methods have provided a boost in various areas such as text classification predicting the next word to language translation. 




[3] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30

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