Due to the increasing presence of robots in several industries, human-robot interaction using natural language has become an important research area. The concept of controlling robots by transforming language instructions in English into executable code for robots is discussed. The proposed approach makes use of semantic similarity by comparing the given instruction with those within a corpus and executes the instruction most similar to the one given. The method is application specific but the modular nature of the system allows it to be adapted for any robot and for any purpose. For this project, a Raspberry Pi based robot following navigation commands is used for experiments and a success rate of 96% is observed.

Prediction of stock markets has been a significant research area. Especially the study of changes in stock prices due to non-quantifiable factors. Here, the concept of fluctuations in the values of stocks due to people's emotional state is explored. In this approach, sentiment analysis is performed on Twitter data (tweets), the results of which are fed into a prediction algorithm along with stock data from Dow Jones Industrial Average (DJIA). Here, sentiment analysis is based on lexicons as well as heuristics and it determines the tweets' emotional polarity and classifies them as either positive or negative. Results obtained show 100% accuracy in mapping the tweets' sentiments to the change in stock prices and the average deviation between predicted and real stock values is 1.77.