Social Media provides an opportunity for people to share their idea about different aspects of life. Traveling is one of the essential aspects of life. In this paper, we use Bidirectional Encoder Representations from Transformers(BERT) for sentiment classification of online reviews to implement a Convolutional Neural Network model that can recommend airline tickets. Different sentiment classification models are compared based on people’s online reviews from six online social platforms. The new model can classify airline tickets as an economical choice or not economical choice ticket based on various aspects of the trips, including airline customer satisfaction, travel destination, hotel information, restaurants, and tourist attractions. This work contribution is two-fold: first, it examines the importance of choosing a transfer learning model for sentiment classification of online social platforms in the recommender system’s prediction accuracy. Second, the implementation of the Convolutional Neural Network model, which can classify airline tickets based on data generated by multiple online social platforms. Using the natural language processing approach to use the transfer learning model to improve the CNN model’s prediction accuracy is a new approach to use online social platforms to recommend economical airline tickets to consumers.

Using Transfer Learning Approach to Implement Convolutional Neural Network model to Recommend Airline Tickets by Using Online Reviews

Pervasive sensing of people's opinions is becoming critical in strategic decision processes, as it may be helpful in identifying problems and strengthening strategies. A recent research trend is to understand users' opinions through a sentiment analysis of contents published in the Twitter platform. This approach involves two challenges: the large volume of available data and the large variety of used languages combined with the brevity of texts. The former makes manual analysis unreasonable, whereas the latter complicates any type of automatic analysis. Since sentiment analysis is a difficult process for computers, but it is quite simple for humans, in this article, we transform the sentiment analysis process into a game. Indeed, we consider the game with a purpose approach and we propose a game that involves users in classifying the polarity (e.g., positive, negative, neutral) and the sentiment (e.g., joy, surprise, sadness) of tweets. To evaluate the proposal, we used a dataset of 52,877 tweets, we developed a Web-based game, we invited people to play the game, and we validated the results through two different methods: ground-truth and manual assessment. The obtained results showed that the game approach is effective in measuring people' sentiments and also highlighted that participants liked to play the game.

Sentiment analysis and Twitter: a game proposal

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.

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Over a Decade of Social Opinion Mining: A Systematic Review

Social networking sites, blogs and many more informal texting sites have taken a center stage in our lives, and in this informal messages we tend to show a lot of emotions towards a certain social cause or towards friends. Quite impressive work is done in emotion analysis or emotion classification area, but we require algorithms to identify emotion strength/intensity from informal and short text messages. Emotion intensity from a short text can help us in identifying the degree of emotion in a sentence belonging to the same emotion category. This paper proposes a novel idea for solving the problem of identification of emotion intensity from social media data.

Emotion intensity detection for social media data

Nowadays, social media have become one of the most important methods of communication that provide a real-time and rich source of information, including sentiments. Understanding the population sentiment is a key goal for organisations and governments. In recent years, quite a lot of research has been done on sentiment analysis from social media. However, all the work in the state of the art is focused on a specific pre-defined subset of tweets, e.g. sentiment analysis via keywords search from tweets for relevant brands, products, services, events and so forth. Monitoring the general sentiment at national level through the whole social media stream is not done due to the challenges of filtering sentiment-irrelevant information, diversity of vocabulary usage in general tweets across topics causing low accuracy and the need for bilingual or multilingual models. This paper proposes a system for general population sentiment monitoring from a social media stream (Twitter), through comprehensive multi-level filters, and our proposed improved latent Dirichlet allocation (LDA) (Wang et al. in ACM Trans Internet Technol 18(1):1–23, 2017; Wang and Al-Rubaie in Appl Soft Comput 33:250–262, 2015; https://patents.google.com/patent/US20170293597A1/en) method for sentiment classification. Experiments show that our proposed improved LDA for sentiment analysis yields the best results, and also validate our proposed system for national sentiment monitoring in Abu Dhabi using twitter.

/pdf/national-happiness-index-monitoring-using-twitter-for-5cm4eto2x3.pdf

National happiness index monitoring using Twitter for bilanguages

In this paper we present a novel approach to Twitter Sentiment Analysis. The approach adopted is to analyse the lexicon features of the tweets for classifying its sentiment (positive, negative and neutral). The training data is made more exhaustive by including various manually labelled tweets, in addition to the existing word stock to keep up with the changing micro logging trends. For Data Preprocessing, a novel spell checking algorithm is introduced, an operation for disjoining compound words such as "high hopes" is implemented and emoticons are replaced by suitable emotion words like happy or sad. After this initial preprocessing, the machine learning algorithms are (Support vector machines and Maximum entropy) are applied. We also propose an avant-garde sentiment scoring mechanism to estimate the degree of the sentiment. Our approach is able to assign sentiments to tweets with an accuracy of 80%.

Twitter Sentiment Analysis -- A More Enhanced Way of Classification and Scoring

The study focuses on the synthesis of polyurethane (PU)-based coating from modified castor oil (CO) via an in situ polymerization process. CO was modified by employing multiple process steps, that included epoxidation, followed by transesterification and acrylation to get the required polyol for PU synthesis. The structural properties were studied using Fourier transform infrared and 1H NMR spectra. Thermogravimetric analysis (TGA) and differential scanning calorimetry revealed an increase in the final degradation temperature and glass transition temperature from 358 and −46.58 °C to 368 and −37.43 °C respectively after transesterification followed by acrylation of epoxidized CO. Hydroxyl value, acid value, amine value and viscosity were observed to change from 208 mg/KOH, 1.75, 13.09 and 428 mPa s to 233 mg/KOH, 1.16, 19.53 and 318 mPa s after transesterification and acrylation of epoxidized CO. Paint formulations at variable OH:NCO ratios like 1:0.9, 1:1 and 1:1.1 were optimized by following the cross-cut tape test, lap-shear test, abrasion resistance test and pencil hardness test. Crosslinking density of the paint samples was calculated and the morphological properties of the optimized coating sample were studied using a scanning electron microscope. TGA was carried out to study the thermal properties of the coating. © 2022 Society of Chemical Industry. 

Synthesis of Castor oil‐based Polyols applicable in Acrylated Polyurethane Coating with Higher Mechanical Properties

The automobile paint industry normally consumes petro-based feedstock as the raw material, which is non-renewable and emits a higher amount of volatile organic compound, that pollutes the environment. This study focuses on the use of eco-friendly castor oil (CO) as a substitute for petro-based feedstocks; while synthesizing polyurethane (PU)-based automobile clear coats. An acrylate-PU/nano-silica (NS) hybrid coating was developed, which was derived from CO-based polyol. CO was modified by employing a process that includes epoxidation, followed by transesterification, and acrylation to synthesize the required polyols, which is used for acrylate-PU paint synthesis via the in-situ polymerization process. Triallyl isocyanurate (TI) modified NS particles were incorporated with the paint matrix by the ultrasonication method to enhance the paint properties. Experimental findings revealed that with the incorporation of NS particles, char residue, Young's modulus, abrasion resistance, and cross-cut adhesion % increased by 71.31%, 42%, 0.28%, and 5% respectively. Also, glass transition temperature, pencil hardness, nanoindentation depth, water contact angle, and cross-linking density were increased from −12.16°C, 2H, 3300 nm, 81.71°, and 0.90 mol/m3 to 1.65°C, 3H, 2000 nm, 92.26°, and 1.78 mol/m3 respectively. The UV-visibility spectra, haze, transmittance, and gloss parameter showed the enhanced optical properties of the paint samples. PU/NS composite coating developed showed equivalent properties as that of the commercial clear coat. 

Highly transparent castor oil‐derived polyurethane/silica nanocomposite coating synthesized by in situ polymerization

Emergence of antibiotics resistance has threatening consequences not only for human health but also for animal health issues in agriculture. Several animal pathogenic bacteria have developed antibiotic resistance and managing same has tremendous cost repercussions and may lead to total harvest loss. Hence in the present study, efforts are made to revitalize an old antibiotic molecule, oxytetracycline (OTc), through nanodelivery approaches using zinc oxide nanoparticles (nZnO) to confront OTc resistant fish pathogenic bacteria Aeromonas hydrophila. OTc was impregnated in nZnO through in situ precipitation method to develop OTc loaded ZnO nanoparticles (OTc@nZnO) with average size of 99.42 nm. Spectroscopic investigation of same revealed complexation of Zn2+ with amide and aromatic carbonyl moieties of OTc [ZnOTc]+. The complex performed better against A. hydrophila with 7-15 mm inhibition zone as compared to nil for bare OTc at same dose. OTc also showed MIC of 150 µg ml-1 and for OTc@nZnO it was 7.02 µg ml-1 with faster killing rate (k, -0.95). In silico docking simulation suggest that [ZnOTc]+ had low binding affinity (LBE > -5.00 kcal mol-1) toward TetR(E) and TetA(E) proteins of A. hydrophila as compared to OTc (LBE < -8.00 kcal mol-1). This study postulates that [ZnOTc]+ released from OTc@nZnO can escape TetR(E) and TetA(E) resistance proteins and bind at 30S ribosomal subunit with high affinity (<-11.00 kcal mol-1) to exert antibacterial properties. In the recent scenario of recurrent antimicrobial resistance, the develop antibiotic-nanocomposites could come out as potential solution, however further study is required for its feasibility for use in animal health care. 

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Antibacterial properties and in silico modeling perspective of nano ZnO transported oxytetracycline-Zn2+ complex [ZnOTc]+ against oxytetracycline-resistant Aeromonas hydrophila

Debasmit Mohanty

Papers

Twitter Sentiment Analysis -- A More Enhanced Way of Classification and Scoring

Synthesis of Castor oil‐based Polyols applicable in Acrylated Polyurethane Coating with Higher Mechanical Properties

Highly transparent castor oil‐derived polyurethane/silica nanocomposite coating synthesized by in situ polymerization

Antibacterial properties and in silico modeling perspective of nano ZnO transported oxytetracycline-Zn2+ complex [ZnOTc]+ against oxytetracycline-resistant Aeromonas hydrophila

Characteristic properties of base coat of automobile paint: enhancement in scratch and abrasion resistance by nanoscale reinforcement—a review