Harnessing Twitter for Automatic Sentiment Identification

Sentiment Analysis is a motivating space of research because of its applications in different fields. Gathering opinions of individuals about products, social and political events, and problems through the web is turning out to be progressively prevalent consistently. People’s opinions are beneficial for the public and for stakeholders when making certain decisions. Opinion mining is a way to retrieve information through search engines, web blogs, micro-blogs, Twitter and social networks. User generated content on Twitter gives an ample source to gathering individuals’ opinion. Due to the gigantic number of tweets as unstructured text, it is difficult to outline the information physically. Accordingly, proficient computational strategies are required for mining and condensing the tweets from corpuses which, requires knowledge of sentiment bearing words. Many computational methods, models and algorithms are there for identifying sentiment from unstructured text. Most of them rely on machine-learning techniques, using Bag-of-Words (BoW) representation as their basis. In this study, we have used lexicon based approach for automatic identification of sentiment for tweets collected from twitter public domain. We have also applied three different machine learning algorithm (Naive Bayes (NB), Maximum Entropy (ME) and Support Vector Machines (SVM)) for sentiment identification of tweets, to examine the effectiveness of various feature combinations. Our experiments demonstrate that both NB with Laplace smoothing and SVM are effective in classifying the tweets. The feature used for NB are unigram and Part-of-Speech (POS), whereas unigram is used for SVM

Multiple Damage Identification of Beam Structure Using Vibration Analysis and Artificial Intelligence Techniques

This thesis investigates the problem of multiple damage detection in vibrating structural members using the dynamic response of the system. Changes in the loading patterns, weakening/degeneration of structures with time and influence of environment may cause cracks in the structure, especially in engineering structures which are developed for prolonged life. Hence, early detection of presence of damage can prevent the catastrophic failure of the structures by appropriately monitoring the response of the system. In recent times, condition monitoring of structural systems have attracted scientists and researchers to develop on line damage diagnostic tool. Primarily, the structural health monitoring technique utilizes the methodology for damage assessment using the monitored vibration parameters. In the current analysis, special attention has been focused on those methods capable of detecting multiple cracks present in system by comparing the information for damaged and undamaged state of the structure. In the current research, methodologies have been developed for damage detection of a cracked cantilever beam with multiple cracks using analytical, Finite Element Analysis (FEA), fuzzy logic, neural network, fuzzy neuro, MANFIS, Genetic Algorithm and hybrid techniques such as GA-fuzzy, GA-neural, GA-neuro- fuzzy. Analytical study has been performed on the cantilever beam with multiple cracks to obtain the vibration characteristics of the beam member by using the expressions of strain energy release rate and stress intensity factor. The presence of cracks in a structural member introduces local flexibility that affects its dynamic response. The local stiffness matrices have been measured using the inverse of local dimensionless compliance matrix for finding out the deviation in the vibrating signatures of the cracked cantilever beam from that of the intact beam. Finite Element Analysis has been carried out to derive the vibration indices of the cracked structure using the overall flexibility matrix, total flexibility matrix, flexibility matrix of the intact beam. From the research done here, it is concluded that the performance of the damage assessment methods depends on several factors for example, the number of cracks, the number of sensors used for acquiring the dynamic response, location and severity of damages. Different artificial intelligent model based on fuzzy logic, neural network, genetic algorithm, MANFIS and hybrid techniques have been designed using the computed vibration signatures for multiple crack diagnosis in cantilever beam structures with higher accuracy and considerably low computational time

Analysis of Adaptive Fuzzy Technique for Multiple Crack Diagnosis of Faulty Beam Using Vibration Signatures

This paper discusses the multicrack detection of structure using fuzzy Gaussian technique. The vibration parameters derived from the numerical methods of the cracked cantilever beam are used to set several fuzzy rules for designing the fuzzy controller used to predict the crack location and depth. Relative crack locations and relative crack depths are the output parameters from the fuzzy inference system. The method proposed in the current analysis is used to evaluate the dynamic response of cracked cantilever beam. The results of the proposed method are in good agreement with the results obtained from the developed experimental setup