Generating Homograph Models in Topic Modeling for Expediting User's Model Selection

The entire globe is responsible for generating large amounts of information every day due to the advent of diverse social media. Due to availability of such unstructured data, it becomes increasingly difficult to fetch relevant information that interests a user. This occurs due to the ever-present homograph words which imply multiple meanings when used under various contexts. Hence a need arises to develop an approach to organize such conflicting information generated due to homographs. Topic modeling is an approach through which we can organize the information. Twitter is one such social media site which greatly challenges the researchers to interpret accurate information. Given that a number of tweets may lead to conflicting and contradictory information as according to each user's interpretation. Hence the authors propose the use of Latent Dirichlet Allocation algorithm and generate all possible meaningful combinations through which the user's can analyze their peer's opinions by choosing the appropriate homograph models.