The research project aims to apply an integrated approach to natural language processing NLP to satisfaction surveys. It will focus on understanding and extracting relevant information from survey responses, analyzing feelings, and identifying recurring word patterns. NLP techniques will be used to determine emotional polarity, classify responses into positive, negative, or neutral categories, and use opinion mining to highlight participants opinions. This approach will help identify the most relevant aspects for participants and understand their opinions in relation to those specific aspects. A key component of the research project will be the analysis of word patterns in satisfaction survey responses using NPL. This analysis will provide a deeper understanding of feelings, opinions, and themes and trends present in respondents responses. The results obtained from this approach can be used to identify areas for improvement, understand respondents preferences, and make strategic decisions based on analysis to improve respondent satisfaction.

Airport service quality evaluation is commonly found on social media, including Google Maps. This valuable for airport management in order to enhance the quality of services provided. However; prior studies either provide general review for topics discussed by travellers or provide sentimental value to tag the entire review without specifically mentioning the airport service that is behind such value. Accordingly, this work proposes using aspect based sentimental analysis in order to provide more detailed analysis for travellers reviews. This works applied aspect based sentimental analysis on data collected from Google Map about Dubai and Doha airports. The results provide tangible reasons to use aspect based sentimental analysis in order to understand more the travellers and spot airport services that are in need for improvement.

This study is main goal is to provide a comparative comparison of libraries using machine learning methods. Experts in natural language processing (NLP) are becoming more and more interested in sentiment analysis (SA) of text changes. The objective of employing NLP text analysis techniques is to recognize and categorize feelings related to twitter users utterances. In this examination, issues with SA and the libraries utilized are also looked at. provides a number of cooperative methods to classify emotional polarity. The Naive Bayes Classifier, Decision Tree Classifier, Maxent Classifier, Sklearn Classifier, Sklearn Classifier MultinomialNB, and other conjoint learning algorithms, according to recent research, are very effective. In the project will use Five Python and R libraries NLTK, TextBlob, Vader, Transformers (GPT and BERT pretrained), and Tidytext will be used in the study to apply sentiment analysis techniques. Four machine learning models Tree of Decisions (DT), Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbor (KNN) will also be used. To evaluate how well libraries for SA operate in the social network environment, comparative study was also carried out. The measures to assess the best algorithms in this experiment, which used a single data set for each method, were precision, recall, and F1 score. We conclude that the BERT transformer method with an Accuracy: 0.973 is recommended for sentiment analysis.

Scientists have developed a new machine learning model that uses social media data to predict and monitor wildfires more accurately in real-time. Data scientists from Imperial's Data Science Institute used machine learning - a subfield of artificial intelligence where computers learn from data and statistics, in a wildfire prediction model. In this new model, they combined social media data and geophysical satellite data to predict wildfire characteristics with high accuracy. The study, published in the Journal of Computational Social Science, demonstrates how social media could be key to making more informed, socially driven decisions which could help disaster management teams to identify areas of immediate danger. The intensity of wildfires and wildfire season length is increasing due to climate change, causing greater threats to populations worldwide.

Over millions of years, humans have adapted mysterious pathways to evolve the art of communication. It all started with gossiping which later enabled us to communicate and convey our messages to other human beings in an effective manner using sound. To narrow it down there are two major factors involved in boosting human evolution, one is language and the other is machines.The industrial revolution made a huge impact on every ecosystem. Alongside humans, machines are also evolving, in the early 80s we had to operate machines mechanically, and later when electronic machines we being designed we started using switches, and now we have to program machines. But only handful of specialized computer scientists can design and program these complicated machines.

NLP stands for Natural Language Processing.It is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. NLP has the ability of a computer to understand, analyze, manipulate, and potentially generate human language. Just 21% of the available data is present in the organized form in the 21st century. Millions of tweets, emails and web searches are generated daily, resulting in a huge amount of data increasing by the minute..And most of these data are in the form of text and unstructure.Natural Language Processing plays an important role in structuring data. Sentimental Analysis is the interpretation and classification of emotions in positive,negative or neutral within the text data using text analysis techniques.

Sentimental Analysis helps in quickly analyzing the numerous amount of data. Since Artificial Intelligence and its advanced technologies have started influencing different sectors. A lot of research work is taking place for developing different tools that can evolve Artificial Intelligence and Machine Learning more stronger. And Sentiment Analysis is one such topic that has created a buzz in the field of scientific and market research in the field of Natural Language Processing and Machine Learning with the help of its amazing applications. Basically, Sentiment Analysis is a Machine Learning tool.

A data-centric approach with Natural Language Processing (NLP) to predict personality types based on the MBTI (an introspective self-assessment questionnaire that indicates different psychological preferences about how people perceive the world and make decisions) through systematic enrichment of text representation, based on the domain of the area, under the generation of features based on three types of analysis: sentimental, grammatical and aspects. The experimentation had a robust baseline of stacked models, with premature optimization of hyperparameters through grid search, with gradual feedback, for each of the four classifiers (dichotomies) of MBTI. The results showed that attention to the data iteration loop focused on quality, explanatory power and representativeness for the abstraction of more relevant/important resources for the studied phenomenon made it possible to improve the evaluation metrics results more quickly and less costly than complex models such as the LSTM or state of the art ones as BERT, as well as the importance of these results by comparisons made from various perspectives. In addition, the study demonstrated a broad spectrum for the evolution and deepening of the task and possible approaches for a greater extension of the abstraction of personality types.

Customer experience is rising to become the top priority of many business companies nowadays. The expectations of these customers with the services provided by an organization are also growing with each passing moment. The need is to have a system that can guarantee improved customer satisfaction, which is where artificial intelligence (AI) plays a key role. With the help historical data, AI is being used to minimize the chances of errors while providing extremely accurate analysis and solutions. More and more organizations are using AI to improve their customer interactions and experiences, by searching for immediate solutions and actions regarding opportunities that can boost customer experience and simultaneously provide them leverage over their respective competitors.

Do you believe in Artificial Intelligence impacts on businesses? Fine, let's sail through the following write-up and learn how it is happening. Yes, we are about to talk on AI's impact on contact centers of businesses. Well, companies measure the customers' satisfaction by analyzing superficial data: gathered from the contact center. This data may include the factors such as how much time a representative spent on a phone call or chat with a particular customer, whether the issue was resolved on the first call, and what is the feedback of the customer at the end of that conversation. However, analysts do not count the efforts the call required on the part of the customer.