World Wide Web is a vast, dynamic and continuously growing collection of web documents. Due to its huge size, it is very difficult for the users to search for the relevant information about a particular topic of interest. In this paper, an improved architecture of focused crawler is proposed, which is a hybrid of various techniques used earlier. The main goal of a focused crawler is to fetch the web documents which are related to a pre-defined set of topics/domains and to ignore the irrelevant web pages. To check the relevancy of a web page, Page Score is computed on the basis of content similarity of the web page with reference to the topic keywords. URLs Priority Queue is implemented by calculating the Link Score of extracted URLs based on URLs attributes. URLs queue is also optimized by removing the duplicate contents. Topic Keywords Weight Table is expanded by extracting more keywords from the relevant pages database and recalculating the keywords weight. The experimental result shows that our proposed crawler has better efficiency than the earlier crawlers.

Sentiment Analysis is the way of gathering and inspecting data based on the personal emotions, reviews, and contemplations. The sentimental analysis is also recognized as opinion mining since it mines the major feature from people opinions. There are various social networking platforms, out of which Twitter is praised by lawmakers, academicians, and journalists for its potential political values. In literature, numerous studies have been performed on the data ecstatic to elections on Twitter. The greater part of them has been on the U.S Presidential Elections where there are two main applicants who fight it out. Since individuals discuss so many political parties and candidates and their prospects too in rendered messages, the issues of distinguishing their political feeling become extensive and fascinating. Consideration of all these aspects along with a sheer volume of data propelled us to look into the data and find interesting inferences in it.
To select the 117 members of the Punjab Legislative Assembly, Legislative Assembly election was held in Punjab, the State of India on 4 February 2017. As per the Election Commission, a total of 1.9 crore voters is eligible for voting in August 2016 in Punjab. The data set that is collected with the help of Twython was analyzed to find out trivial things and interesting patterns in the data. The central idea of this research paper is to carry out the sentiment analysis on Legislative Assembly election 2017 that was held in the Punjab, a state of India for the Political Parties like BJP, INC, and AAP. We have analyzed and fetch significant implications from the tweets gathered over the whole period of elections.