1. A method for generating sentiment-based summaries, comprising:
receiving, by a text analyzer, a block of text comprising a plurality of words indicating a user review;
generating, by the text analyser, one or more vectors respectively for the plurality of words in the block of text;
identifying, by the text analyzer, a relation among the one or more vectors using a trained model for determining at least one sentiment associated with the block of text from a group of sentiments comprising at least a positive sentiment, a negative sentiment and a neutral sentiment, wherein one or more training vectors corresponding to a plurality of words of a training text are used for generating the trained model;
associating, by the text analyser, the one or more words to at least one of the sentiments determined;
classifying, by the text analyzer, the one or more words into one or more categories based on the determined at least one sentiment;
and
generating, by the text analyzer, a summary in natural language for each of the one or more categories based on the one or more words classified in the at least one sentiment.
2. The method as claimed in claim 1, wherein the one or more vectors indicate a context of the respective word, semantic of the respective word, syntax similarity of the respective word and a relationship of the respective word with other plurality of words in the block of text.
3. The method as claimed in claim 1, wherein the one or more training vectors are provided as inputs for generating the trained model, wherein the trained model is at least a Long Short-Term Memory (LSTM) model and a Bidirectional-LSTM model.
4. The method as claimed in claim 3, wherein at least the LSTM and the Bidirectional-LSTM models are trained to generate a context vector indicating a context of the user review, wherein the context vector is used to determine a sentiment associated with a plurality of block of test data comprising texts.
5. The method as claimed in claim 4, wherein the LSTM and the Bidirectional-LSTM models use an encoder-decoder model for generating the context vector using the one or more vectors

and an output sequence using the context vector, wherein the output sequence indicates the sentiment associated with the block of text.
6. A text analyzer for generating sentiment-based summaries, comprising:
a communication module configured to receive a block of text comprising a plurality of words indicating a user review;
a sentiment analysis module configured to:
generate one or more vectors respectively for the plurality of words in the block of text; and
identify a relation among the one or more vectors using a trained model for determining at least one sentiment associated with the block of text from a group of sentiments comprising at least a positive sentiment, a negative sentiment and a neutral sentiment, wherein one or more training vectors corresponding to a plurality of words of a training text are used for generating the trained model; a classification module configured to:
associate the one or more words to at least one sentiment determined; and
classify the one or more words into one or more categories based on the determined at least one sentiment; and
a summary generation module configured to generate a summary in natural language for each of the one or more categories based on the one or more words classified in the at least one sentiment.
7. The text analyzer as claimed in claim 6, wherein the sentiment analysis module is configured to generate the one or more training vectors, wherein the one or more training vectors are provided as inputs for generating the trained model, wherein the trained model is at least a Long Short-Term Memory (LSTM) model and a Bidirectional-LSTM model.
8. The text analyzer as claimed in claim 7, wherein the sentiment analysis module is configured to generate a context vector indicating a context of the user review using at least the LSTM and the Bidirectional-LSTM models, wherein the context vector is used to determine a sentiment associated with a plurality of block of test data comprising texts.
9. The text analyzer as claimed in claim 6, wherein the summary generation module comprises an encoder-decoder model for generating a context vector using the one or more vectors and

an output sequence using the context vector, wherein the output sequence indicates the sentiment associated with the block of text.