Aspect Oriented Sentiment Analysis helps us in understanding the sentiment of a customer, regarding a specific aspect of a product. For example, the sentiment of a customer towards a mobile phone may be negative from the perspective of battery life, and positive from the perspective of sound quality. AOSA can be used to understand gather feedback about various features of a product or a service. This may be helpful in focusing on the right features, while trying to improve the product. In addition, this can be helpful in positioning as well. For example, we can use it to monitor the sentiments expressed by customers on competing products, and select the right positioning strategy: what to focus on and what not to focus on.

Up until recently, the text summarization was accomplished using two steps. #1 identify the key sentences #2 put them together to generate the summary. In the 1950s, researchers manually crafted rules to identify the key sentences. In the 1990s, researchers used ML algorithms to craft rules to identify the key sentences. In the 2000s, researchers used graph algorithms to identify key sentences, after representing the text as a graph! In the 2010s, researchers used neural networks to identify the key sentences. From 2017 and onwards, researches having been using a neural network architecture called Transformer to generate new text that amazingly summarizes the original document.