Communication in a Global Village

Communication in a Global VillageInternet has changed this world into the Global village. Communication is the only way to survive. at that place be several ways and channels to hap separately other. Nowadays, we be communicating with for severally one other through different mediums like text messages, percentage and video calls etc. Chatting is one of them. Understanding the moods each other can be a strong tool for better relationships. We often start blabting with come out erudite mood of our opponent and may get unpredict adequate responses. To avoid this we can start a topic according to the mood. To overcome this issue, a guileless technique is proposed in this study. This study is undertaken to create an effective environment by chatting where chatting is done through voice, the voice impart be converted into text then(prenominal) applying simple techniques of data mining with Nave Bayes, the emotions of the opponent will be sensed.INTRODUCTIONChatting through t ext is common today we may not be able to judge other persons current mood and we might start such a topic which does not suits other persons mood. This paper presents an approach to emotion love that assesses the content from textual messages. In this paper, the emotion estimation module is applied to text messages produced by a chat system and text messages coming from the voice-recognition system.Our documental is to adapt a multimedia presentation by detecting emotions contained in the textual information through thematic analysis we can regularise how to communicate with fellow. The estimation of emotions or identification of personalities in chat rooms has several advantages mainly guarding the chatters from conflicting personalities and matching people of similar interests.2. Materials and Methods2.1 Related Work rophy of work has been done for identification of emotions from text. Approaches that exist can be categorized 1 into non-verbal, semantic and symbolic.Textual ch at messages are automatically converted into spoken communication and then instance vectors are generated from frequency counts of speech phonemes present in each message. In combination with other statistically derived attributes, the instance vectors are manipulationd in various machine-learning frame whole works to build classifiers for emotional content.1. Anjo Anjewierden, Bas Kolloffel, and Casper Hulshof 4 derived two models for classifying chat messages using data mining techniques and tested these on an actual data set. The reliability of the classification of chat messages is established by comparing the models performance to that of humans.2.2 Java Speech APIJava Speech API 7 contains speech synthesis and speech recognition. Speech Recognition technology works by converting audio input containing speech into text. It has several phases through which speech is converted into text with some accuracy. Also some third party API is similarly available on the basis of Java S peech API.2.3 Bayesian NetworkClassification is a basic task in data analysis and practice recognition that requires the construction of a classifier, that is, a function that assigns a class label to instances described by a set of attributes. The induction of classifiers from data sets of pre classify instances is a central problem in machine learning. Numerous approaches to this problem are based on various functional representations such as finding trees, decision lists, neural networks, decision graphs, and rules 5.3. Chat sense Mapper CHATEM3.1 ApproachThe current approach will first convert voice into text. Early speech recognition systems tried to apply a set of grammatical and syntactical rules speech. If the crys spoken fit into a certain set of rules, the program could determine what the words were. However, human language has numerous exceptions to its own rules, even when its spoken consistently.In 6 facial expressions are engagementd to communicate emotions. Toda ys speech recognition systems use powerful and complicated statistical modeling systems. These systems use probability and mathematical functions to determine the most likely outcome. The two models that dominate the field today are the Hidden Markov Model and neural networks. These methods involve complex mathematical functions, but essentially, they take the information known to the system to figure out the information hush-hush from it. The Hidden Markov Model is the most common, so well take a closer look at that process. During this process, the program assigns a probability score to each phoneme, based on its built-in dictionary and user training. There is some art into how one selects, compiles and prepares this training data for digestion by the system and how the system models are tuned to a particular application.3.2 Processes3.2.1 Parsing PhaseThe first stage after receiving an input sentence is to create a parse tree using the Stanford Parser. The parser works out the g rammatical structure of sentences, for instance which groups of words go together as phrases and which word is the subject or the object of a verb. We also analyze it in order to find if there is a negation.3.2.2 Emotion Extraction PhaseAt this phase we assign every word with an object that will discernment the following information array of emotions (happiness, sadness, anger, fear, surprise and disgust), negation information, the dominant emotion of the word and the word itself. Once weve established the POS type for each word in the sentence, we actuate by extracting the possible senses hidden behind each word using 3 Jwordnet ( JWordNet is a large lexical database of English) In this database, nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms called synsets, each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations, resulting in the formation of a network of substancefully related words an d concepts to construct a mapping between synset offsets from WordNet, and one of the possible emotion types. In order to do that, we needed to choose base words that will represent each of the emotion types. At the end of this stage we now know which of the synsets has an emotional value as described above, allowing us to update the emotion array of the object holding the word being analyzed, and eventually assign a word with its most probable emotional sense out of the possible emotional senses available.3.2.3 Negation undercover workThe intuitive way to deal with negation is to emphasis the counter emotion of the emotion found as most dominant in the word. For example Happy and Sad, the negation will turn a word marked with emotional value Happy, to be marked with emotional value Sad and vice versa.3.2.4 Sentence TaggingThe method we use to deal with multi-emotional sentence is When we reach a word with an emotional value, we open an appropriate tag and close this tag either whe n we reach a word with a different emotional value, or at the end of the sentence. In case we reached a word with a different emotional value, we open a new emotion tag and in case that the emotional value is similar to the previous one, we continue on to the rest of the sentence.Discussion and Conclusion higher up mentioned technique was repeatedly applied to different group of users, we come to know that, Java Speech API was not accurate 100% and there was limitation and ab initio results were not appealing, but it performed well on chatting done using text messages.Future Research WorkIn our future work, we plan to improve the Emotion Estimation module, e.g. by integrating the recorded user (client) information into the analysis of emotions. According to 2, past emotional states could be important parameters for deciding the affective meaning of the users current message. Some analysis of voice features like pitch, frequency and tone can help us to identify emotions and mode of user.