Abstract

Twitter is a micro blogging website, where users can post messages in very short text called Tweets. Tweets contain user opinion and sentiment towards an object or person. This sentiment information is very useful in various aspects for business and governments. In this paper, we present a method which performs the task of tweet sentiment identification using a corpus of pre-annotated tweets. We present a sentiment scoring function which uses prior information to classify (binary classification) and weight various sentiment bearing words/phrases in tweets. Using this scoring function we achieve classification accuracy of 87% on Stanford Dataset and 88% on Mejaj dataset. Using supervised machine learning approach, we achieve classification accuracy of 88% on Stanford dataset.

Abstract

Micro-blogging sites such as Facebook, Twitter, Google+ present a nice opportunity for targeting advertisements that are contextually related to the microblog content. By virtue of the sparse and noisy text makes identifying the microblogs suitable for advertising a very hard problem. In this work, we approach the problem of identifying the microblogs that could be targeted for advertisements as a twostep classification approach. In the first pass, microblogs suitable for advertising are identified. Next, in the second pass, we build a model to find the sentiment of the advertisable microblog. The systems use features derived from the Partof-speech tags, the tweet content and uses external resources such as query logs and n-gram dictionaries from previously labeled data. This work aims at providing a thorough insight into the problem and analyzing various features to assess which features contribute the most towards identifying the tweets that can be targeted for advertisements.

Abstract

Web page classification has wide number of applications in the area of Information Retrieval. It is a crucial part in building domain specific search engines. Be it’Google Scholar’to search for scholarly articles or’Google news’ to search for news articles, searching within a specific domain is a common practice. Sandhan is one such project which offers domain specific search for Tourism and Health domains across 10 different Indian Languages. Much of the accuracy of a web page classification algorithm depends on the data it gets trained on. The motivation behind this paper is to provide a proper set of guidelines to collect and store this data in an efficient and an error free way. The major contribution of this paper would be a Hindi web page collection manually classified into Tourism, Health and Miscellaneous.

Abstract

With recent developments in web technologies, percentage web content in Hindi is growing up at a lighting speed. This information can prove to be very useful for researchers, governments and organization to learn what’s on public mind, to make sound decisions. In this paper, we present a graph based wordnet expansion method to generate a full (adjective and adverb) subjective lexicon. We used synonym and antonym relations to expand the initial seed lexicon. We show three different evaluation strategies to validate the lexicon. We achieve 70.4% agreement with human annotators and∼ 79% accuracy on product review classification. Main contribution of our work

Abstract

The number of images on the World Wide Web has been increasing tremendously. Providing search services for images on the web has been an active research area. Web images are often surrounded by different associated texts like ALT text, surrounding text, image filename, html page title etc. Many popular internet search engines make use of these associated texts while indexing images and give higher importance to the terms present in ALT text. But, a recent study has shown that around half of the images on the web have no ALT text. So, predicting the ALT text of an image in a web page would be of great use in web image retrieval. We propose an approach on top of term cooccurrence approach proposed in the literature to ALT text prediction. Our results show that our approach and the simple term co-occurrence approach produce almost the same results. We analyze both the methods and describe the usage of the methods in different situations. We build an image annotation system on top of our proposed approach and compare the results with the image annotation system built on top of the term co-occurrence approach. Preliminary experiments on a set of 1000 images show that our proposed approach performs well over the simple term co-occurrence approach for web image annotation.

Abstract

In this paper, we report our participation in Knowledge Base Population at TAC 2012. We adopted an Information Retrieval based approach for the Entity Linking and Slot Filling tasks. In Entity Linking we identify potential nodes from the Knowledge Base and then identify the mapping node using tf-idf similarity. We achieved very good performance in the Entity Linking task. For Slot Filling task we identify documents from the document collection that might contain attribute information. We extract the attribute information using a rule based approach. Our rule based approach hasnt performed up to the mark.

Abstract

With the induction of UTF-8 unicode standards, web content in Hindi language is increasing at a rapid pace. There is a great opportunity to mine the content and get insight of sentiments and opinions expressed by people and various communities. In this paper, we present a graph based method to build a subjective lexicon for Hindi language, using WordNet as a resource. Our method takes a pre-annotated seed list and expands this list into a full lexicon using synonym and antonym relations. We show two different evaluation strategies to validate the Hindi Lexicon built. Main contribution of our work 1) Developing a Subjective lexicon of adjectives using Hindi WordNet. 2) Developing an annotated corpora of Hindi reviews.

Abstract

With the recent emergence of cloud computing based services on the Internet, MapReduce and distributed file systems like HDFS have emerged as the paradigm of choice for developing large scale data intensive applications. Given the scale at which these applications are deployed, minimizing power consumption of these clusters can significantly cut down operational costs and reduce their carbon footprint—thereby increasing the utility from a provider’s point of view. This paper addresses energy conservation for clusters of nodes that run MapReduce jobs. The algorithm dynamically reconfigures the cluster based on the current workload and turns cluster nodes on or off when the average cluster utilization rises above or falls below administrator specified thresholds, respectively. We evaluate our algorithm using the GridSim toolkit and our results show that the proposed algorithm achieves an energy reduction of 33% under average workloads and up to 54% under low workloads.

Abstract

We consider AESOP Task as Topic based evaluation of information content. Means at first stage, we identify the topics covered in given model/reference summary and calculate their importance. At the next stage, we calculate the information coverage in test/machine generated summary, wrt every identified topic. We use the local importance of words in calculation of importance of topics. From experiments it is clear that use of different methods for identification of topics and calculation of information coverage in test documents wrt every identified topic, have different effect on the result. It is important to note that our devised system do not require any linguistic support or learning or training in entire execution of the system.

Abstract

MapReduce framework has received a wide acclaim over the past few years for large scale computing. It has become a standard paradigm for batch oriented workloads. As the adoption of this paradigm has increased rapidly, scheduling of these MapReduce jobs has become a problem of great interest in research community. We propose an approach which tries to maintain harmony among the jobs running on the cluster, and in turn decrease their runtime. In our model, the scheduler is made aware of different types of jobs running on the cluster. The scheduler tries to allocate a task on a node if the incoming task does not affect the tasks already running on that node. From the list of available pending tasks, our algorithm selects the one that is most compatible with the tasks already running on that node. We bring up heuristic and machine learning based solutions to our approach and try to maintain a resource balance on the cluster by not overloading any of the nodes, thereby reducing the overall runtime of the jobs. The results show a saving of runtime of around 21% in the case of heuristic based approach and around 27% in the case of machine learning based approach when compared to Yahoo's Capacity scheduler.