Combining granularity-based topic-dependent and topic-independent evidences for opinion detection

Fouille des opinion, une sous-discipline dans la recherche d'information (IR) et la linguistique computationnelle, fait référence aux techniques de calcul pour l'extraction, la classification, la compréhension et l'évaluation des opinions exprimées par diverses sources de nouvelles en ligne, social commentaires des médias, et tout autre contenu généré par l'utilisateur. Il est également connu par de nombreux autres termes comme trouver l'opinion, la détection d'opinion, l'analyse des sentiments, la classification sentiment, de détection de polarité, etc. Définition dans le contexte plus spécifique et plus simple, fouille des opinion est la tâche de récupération des opinions contre son besoin aussi exprimé par l'utilisateur sous la forme d'une requête. Il y a de nombreux problèmes et défis liés à l'activité fouille des opinion. Dans cette thèse, nous nous concentrons sur quelques problèmes d'analyse d'opinion. L'un des défis majeurs de fouille des opinion est de trouver des opinions concernant spécifiquement le sujet donné (requête). Un document peut contenir des informations sur de nombreux sujets à la fois et il est possible qu'elle contienne opiniâtre texte sur chacun des sujet ou sur seulement quelques-uns. Par conséquent, il devient très important de choisir les segments du document pertinentes à sujet avec leurs opinions correspondantes. Nous abordons ce problème sur deux niveaux de granularité, des phrases et des passages. Dans notre première approche de niveau de phrase, nous utilisons des relations sémantiques de WordNet pour trouver cette association entre sujet et opinion. Dans notre deuxième approche pour le niveau de passage, nous utilisons plus robuste modèle de RI i.e. la language modèle de se concentrer sur ce problème. L'idée de base derrière les deux contributions pour l'association d'opinion-sujet est que si un document contient plus segments textuels (phrases ou passages) opiniâtre et pertinentes à sujet, il est plus opiniâtre qu'un document avec moins segments textuels opiniâtre et pertinentes. La plupart des approches d'apprentissage-machine basée à fouille des opinion sont dépendants du domaine i.e. leurs performances varient d'un domaine à d'autre. D'autre part, une approche indépendant de domaine ou un sujet est plus généralisée et peut maintenir son efficacité dans différents domaines. Cependant, les approches indépendant de domaine souffrent de mauvaises performances en général. C'est un grand défi dans le domaine de fouille des opinion à développer une approche qui est plus efficace et généralisé. Nos contributions de cette thèse incluent le développement d'une approche qui utilise de simples fonctions heuristiques pour trouver des documents opiniâtre. Fouille des opinion basée entité devient très populaire parmi les chercheurs de la communauté IR. Il vise à identifier les entités pertinentes pour un sujet donné et d'en extraire les opinions qui leur sont associées à partir d'un ensemble de documents textuels. Toutefois, l'identification et la détermination de la pertinence des entités est déjà une tâche difficile. Nous proposons un système qui prend en compte à la fois l'information de l'article de nouvelles en cours ainsi que des articles antérieurs pertinents afin de détecter les entités les plus importantes dans les nouvelles actuelles. En plus de cela, nous présentons également notre cadre d'analyse d'opinion et tâches relieés. Ce cadre est basée sur les évidences contents et les évidences sociales de la blogosphère pour les tâches de trouver des opinions, de prévision et d'avis de classement multidimensionnel. Cette contribution d'prématurée pose les bases pour nos travaux futurs. L'évaluation de nos méthodes comprennent l'utilisation de TREC 2006 Blog collection et de TREC Novelty track 2004 collection. La plupart des évaluations ont été réalisées dans le cadre de TREC Blog track.Opinion mining is a sub-discipline within Information Retrieval (IR) and Computational Linguistics. It refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online sources like news articles, social media comments, and other user-generated content. It is also known by many other terms like opinion finding, opinion detection, sentiment analysis, sentiment classification, polarity detection, etc. Defining in more specific and simpler context, opinion mining is the task of retrieving opinions on an issue as expressed by the user in the form of a query. There are many problems and challenges associated with the field of opinion mining. In this thesis, we focus on some major problems of opinion mining

A Korovkin Type Approximation Theorem and Its Applications

We present a Korovkin type approximation theorem for a sequence of positive linear operators defined on the space of all real valued continuous and periodic functions via A-statistical approximation, for the rate of the third order Ditzian-Totik modulus of smoothness. Finally, we obtain an interleave between Riesz's representation theory and Lebesgue-Stieltjes integral-i, for Riesz's functional supremum formula via statistical limit

High-Speed Implementations Of Fractal Image Compression For Low And High Resolution Images

Fractal Image Compression (FIC) is a very popular coding technique that is used in image/video applications due to its simplicity and superior performance. The major drawback of FIC is that it is a time consuming algorithm, especially when a full search is attempted. Hence, it is very challenging to achieve a real-time operation especially when this algorithm is run on a general or graphic processor unit. Therefore, in this research new hardware implementations of FIC are proposed for accelerating the encoding process by means of parallelism and pipelining. Various approaches have been investigated for achieving high speed performance. The computational complexity of fractal operations are first investigated in order to select the minimum and efficient bit sizes that can provide similar or nearly similar encoding quality. This has resulted in a relatively new FIC hardware which is referred in this thesis as Design I. In this design, a full-search approach was adopted in order to enable reconstruction at highest possible quality. However, full-search scheme is not suitable for encoding larger images since the encoding time is increased dramatically when processing high-resolution images. This problem is solved in Design II which used a partial-search based scheme in order to achieve high-speed operation. This method exploits the inherently high degree of correlation between pixels in the neighbourhood areas in digital image to restrict the search space to those areas. By fixing these areas for each group of range blocks and partitioning an image in which each domain block contains four range blocks, enabled two matching operations be performed simultaneously. This reduced the memory access by half, thereby, doubling the speed by a factor of 2. This design was extended to encode RGB image, resulting in another new design referred to as Design III. In this design, the strong cross-correlation between the image components was exploited so that only the G component was encoded using the same approach as in Design II, while the R and B components were encoded by searchless-based scheme with direct mapping between overlapped blocks. All three designs were examined in terms of runtime, peak-signal-to-noise-ratio (PSNR), and compression rate. The experimental results of Design I when implemented in Altera Cyclone II FPGA, showed speedup of 3 times, on average, while the PSNR was not significantly affected. Empirical results demonstrated that this firmware is competitive when compared to other existing full-search hardware with PSNR averaging at 30 dB, 5.82 % compression rate and a runtime of 9.8 ms. On the other hand, Design II was synthesised on Altera Stratix IV FPGA and showed an ability to encode a 1024×1024 image at 395 MHz in 10.8 ms with PSNR averaging at 27 dB and compression rate of 34. These results suggest that the proposed approach enables colour images be encoded at approximately same speed as grayscale images. Also the proposed architectures have achieved better performance compared to the state-of-the-art designs, with speed averaging at 100, 92 and 83 fps for Design I, II and III respectively

Effectiveness gain of polarity detection through topic domains

National audienceMost of the work on polarity detection consists in finding out negative or positive words in a document using sentiment lexical resources. Indeed, some versions of such approaches have performed well but most of these approaches rely only on prior polarity of words and do not exploit the contextual polarity of words. Sentiment semantics of a term vary from one domain to another. For example, the word "unpredictable" conveys a positive feeling about a movie plot, but the same word conveys negative feeling in context of operating of a digital camera. In this work, we demonstrate this aspect of sentiment polarity. We use TREC Blog 2006 Data collection with topics of TREC Blog 2006 and 2007 for experimentation. The results of our experiments showed an improvement (95%) on polarity detection. The conclusion is that the context plays a role on the polarity of each word

Opinion mining: Reviewed from word to document level

International audienceOpinion mining is one of the most challenging tasks of the field of information retrieval. Research community has been publishing a number of articles on this topic but a significant increase in interest has been observed during the past decade especially after the launch of several online social networks. In this paper, we provide a very detailed overview of the related work of opinion mining. Following features of our review make it stand unique among the works of similar kind: (1) it presents a very different perspective of the opinion mining field by discussing the work on different granularity levels (like word, sentences, and document levels) which is very unique and much required, (2) discussion of the related work in terms of challenges of the field of opinion mining, (3) document level discussion of the related work gives an overview of opinion mining task in blogosphere, one of most popular online social network, and (4) highlights the importance of online social networks for opinion mining task and other related sub-tasks

FREQUENCY OF HEPATITIS-B AND C IN PATIENTS UNDERGOING CATARACT SURGERY IN A TERTIARY CARE HOSPITAL, DERA GHAZI KHAN

Objective: To determine the frequency of hepatitis B and C in patients undergoing elective cataract surgery. Study design: A descriptive study. Place of study: Ophthalmology Department, Teaching Hospital Dera Ghazi Khan. Duration of study: From 1st February 2017 to 30th September 2017. Methodology: All patients admitted for elective cataract surgery were included in the study. Screening was done for hepatitis B and C and findings were recorded. Hepatitis positive cases were identified and demographic data was collected on structured compilation sheets and analysis done. Results: A total of 889 patients were included in study. Overall, 52(5.85%) cases were positive for viral hepatitis B and C infection. 18(2.03%) were positive for hepatitis B and 34(3.82%) were positive for hepatitis C. 24 out of 52 cases (46.15%) were from rural areas and 28 out of 52 cases (53.85%) were from urban population. Conclusion: A significant number of hepatitis B and C positive cases were seen in patients admitted for elective cataract surgery. It is highly recommended that screening of preoperative cases of cataract surgery should be done so that even asymptomatic patients should pose no more threat to the spread of the disease. Key Words: Hepatitis B, Hepatitis C, Cataract surgery