We Propose A Novel Framework For Tweet Segmentation

Proclaimed as the hottest company since Google and Facebook, Twitter introduced a revolutionary micro-blogging service in 2006 that allowed users to spread and share short messages of 140 characters (“tweets”) with friends and strangers subscribing to follow their communication flow (as so called “followers”) in order to find out what is happening right now from any point of the globe.

Twitter is one of the most popular social media outlets in the world, having the ability to connect people from all over the world. Though the platform may have its perks, problems such as being limited to using very few characters, being fooled by fraudulent users, and being a detriment to your future career are all risks that one must choose to take. While Twitter may offers many benefits, one must realize all of the consequences that they may be faced with when joining the social media

Twitter one of the most popular social networking sites. Both personal and corporate users enjoy twittering to engage in information-sharing. It has social as well as commercial utility. However, as is also the case with Facebook, Twitter is still striving to make its business model profitable as well as generate user traffic. Advertisers and marketers are interested in Twitter's ability to provide data about potential customers as well as its ability to expose users to advertising. However, there have been a number of concerns regarding the monetization of Twitter. Twitter's potential for 'data-mining' is alarming to many users. "By virtue of having a large number of users, Twitter also possessed such a database of personal information, as well as a large archive of personal messages" (Privacy issues and monetizing Twitter, 2011, Richard Ivey School of Business: 6).

On the rainy Wednesday I learned that when there is bad weather the park has three options for running operations. Option #1 is that Tweetsie is run as normal and the show goes on as usual. Option #2 is running at half capacity, which means that only Main Street and the train will operate. All of the rides stop running and none of the shows take place during operating hours. Finally, Tweetsie has the option of just running train rides and closing all other aspects of the park. If this happens we let people in for free because all they would be able to do is ride the train. In the recent history of Tweetsie they’ve only closed twice and run a half-day once, so it’s common to keep the park open despite nasty weather. I also learned not to get my hopes up that I wouldn’t have to go into work if it’s raining in the morning!

Manual segmentation of this CT scans are tedious and prohibitively time-consuming for a clinical setting. Automatic segmentation on the other hand, is a very challenging task, due to various factors, such as liver stretch over 150 slices in a CT image, indefinite shape of the lesions and low intensity contrast between lesions and similar to those of nearby tissues. The irregularity in the liver shape and size between the patients and the similarity with other organs of almost same intensity make automatic liver segmentation

As speed is a large part of why a new solution was needed, proper testing is needed to determine which solution is faster. The test was to search a reasonable large test set. In this case, the test data is roughly 4 million tweets from Twitter [12], which represents roughly 470 megabytes of data. This test data was chosen as tweets tend to be more conversational compared to newspaper articles, it was large at 4 million,

The clustering hierarchy for text clustering or documents clustering we used cluster dendrogram presentation by R implementation. Some people find cluster dendrograms to be fairly clear to read. Others simply find them perplexing. Here, we can see many more groups that are identifiable in the dendrogram. Here, I have arbitrarily chosen to look at five clusters, as indicated by the red boxes in figure

A statistical validation process is performed to check whether non-related movie tweets are included in the results of the parsing process or not. The validation process is taken into account in step 1.2 of the methodology. Sample tweets are selected with the margin of error (5%) and confidence level (95%) to determine the proper sample size for the population (the raw data). The result of validation process, 89.22 percent tweets are confirmed as related the movies. Entire results of the validation process in the case study are illustrated on Table

As in our study, LDA topics has improved accuracy of finding the keywords for different topics.In this work we examine the social aspects of food tweeting behavior, and provide some support to the social affinity that is not local in geographic sense. There have been several recent studies that probe the viability of public health surveillance by measuring relevant textual signals in social media.Prier, K.W.Smith, M.S.Giraud-Carrier, C. L. Hanson[5] examine all words people use in online reviews, and draw insights on correlating terms and concepts that may not seem immediately relevant to the hygiene status of restaurants. The work draws from the rich body of research that studies online reviews for sentiment analysis based on few research papers.

Later we divided the BPD tweets into two segments 1. Incident related (The tweets by BPD which spoke about the incident and things related to it) 2. Informational (The tweets by BPD which were sent out to public about the road being blocked or about parking related etc.). The reason for dividing into 2 segments was to further observe the difference of impact on users by these tweets, with the help of sentiment scores generated

The first problem was to find all of these customers who were expressing their “pain” on the web and then to allocate their attention on to them. The second problem was to develop a process that operationalized the problem resolution and made it traceable and scalable. To overcome these problems, Debatescape used powerful tools such as RSS feeds, open application programming interfaces (APIs), and content-scraping tools to obtain and pool user-generated content from online forums, blogs, and social networking sites such as Twitter, Facebook and YouTube. However, this content needs to be classed in different categories. Debatescape uses a natural language analyzer to help classify the content into various categories to be then sorted and sent to the correct customer service agent in either BT Consumer or BT Business department. Afterwards, BT agents

Facebook’s DeepText is a close resemblance of Google’s NMT(Neural Machine Translation). Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference – sometimes prohibitively so in the case of very large data sets and large models. Several authors have also charged that NMT systems lack robustness, particularly when input sentences contain rare words. These issues have hindered NMT’s use in practical deployments and services, where both accuracy and speed are essential. In this work, we

Probabilistic topic modeling provides computational methods for large text data analysis. Today streaming text mining plays an important role within real-time social media mining. Latent Dirichlet Allocation (LDA) model was developed a decade ago to aid discovery of the hidden thematic structure in large archives of documents. It is acknowledged by many researchers as the most popular approach for building topic models. In this study, we discuss topic modeling and more specifically LDA. We identify speed as one of the major limitations of LDA application in streaming big text data analytics. The main aim of this study is to enhance inference speed of LDA thereby develop a new inference method and algorithm. Given the characteristics of this specific research problem, the approach to the proposed research will follow the experimental model. We will investigate causal relationships using a test

Segmentation of handwritten document images into text-lines and words is a necessary job for optical character recognition. Conversely, since the topographies of handwritten document are asymmetrical and miscellaneous reliant on the person, it is considered a thought-provoking problem. To address the problem, we articulate the word segmentation problem as a Binary Quadratic Assignment (BQA) problem that contemplates pairwise correspondences amongst the gaps in addition to the likelihoods of distinct gaps. Although many attributes are involved in our articulation, we guesstimate all attributes depending on the Structured SVM (Support vector machine) framework so that the anticipated method works fine irrespective of writing styles and written languages deprived of user-0 defined attributes. Experimental results on ICDAR 2009/2013 handwriting segmentation databases displays that anticipated method attains the hi-tech performance on

Abstract: Relation classification is a keynote in the field of Natu-ral Language Processing (NLP) to mine information from text facing problems of over-reliance on the standard of handcrafted features. Features annotated by specialists and lin-guistic data derived from linguistic analysis modules is expen-sive and ends up with the difficulty of error propagation. Rela-tion extraction plays a crucial role in extracting struc-tured data from unstructured sources like raw text. One might want to seek out interactions between medicines to create medical information or extract relationships among people to create a simply searchable knowledgebase. We propose a deep Convolutional Neural Network model for the multi-label text relation classification task without hand crafted features. This model outperforms the best existing model as per our knowledge without depending much on manually engineered features with the small updates in the loss function applied.