Rakhi Batra
Sukkur IBA, Oric, Department Member
Research Interests:
Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization, person or any other entity. Sentiment Analysis can be used to... more
Sentiment Analysis is new way of machine learning to extract opinion orientation (positive, negative, neutral) from a text segment written for any product, organization, person or any other entity. Sentiment Analysis can be used to predict the mood of people that have impact on stock prices, therefore it can help in prediction of actual stock movement. In order to exploit the benefits of sentiment analysis in stock market industry we have performed sentiment analysis on tweets related to Apple products, which are extracted from StockTwits (a social networking site) from 2010 to 2017. Along with tweets, we have also used market index data which is extracted from Yahoo Finance for the same period. The sentiment score of a tweet is calculated by sentiment analysis of tweets through SVM. As a result each tweet is categorized as bullish or bearish. Then sentiment score and market data is used to build a SVM model to predict next day's stock movement. Results show that there is positive relation between people opinion and market data and proposed work has an accuracy of 76.65% in stock prediction.
Research Interests:
This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing... more
This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing which includes removing columns containing user information, retweet’s count, followers information, duplicate tweets, removing unnecessary punctuation, links, symbols, and spaces, and finally extracting emojis if present in the tweet text. In the final dataset each tweet record contains columns for tweet id, text, and emoji extracted from the text with a sentiment score. Emojis are extracted to validate Machine Learning models used for the multilingual sentiment and behavior analysis. These are extracted using a Python script that searches for an emoji from the list of 751 most frequently used emojis. If an emoji is present in the text, a column with the emoji description and sentiment score is added.
It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic... more
It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users’ tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity t...
Research Interests:
Cell phones have turn out to be the most central communication gadget in our daily life. This results in an enormously intense competition between almost all the mobile phone vendors. Despite of manufacturer’s diverse types of advertising... more
Cell phones have turn out to be the most central communication gadget in our daily life. This results in an enormously intense competition between almost all the mobile phone vendors. Despite of manufacturer’s diverse types of advertising strategies such as exceptional price cut offers or modern attractive functions, what really matter is whether this everyday communication gadget has been designed according to the preference and requirements of all types of users. The miniature type screen interface design is one of the recent research themes of the Human-Computer Interaction domain. Because of the restricted screen size, “icons” have been considered as dominant part in usability of cell phones. This paper measures the recognition level of icons among e-literate and non-e-literate people. This article explores the effect of icon characteristic on recognition level of icons among e-literates and non e-literate users. It was found that designers of mobile phone icons have to balance ...