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Twitter is the most widely used platform with an open network; hence, tourists often resort to Twitter to share their travel experiences, satisfaction/dissatisfaction and other opinions. This study is divided into two sections, first to provide a framework for understanding public sentiments through Twitter for tourism insights, second to provide real-time insights of three Indian heritage sites i.e., the Taj Mahal, Red Fort and Golden Temple by extracting 5,000 tweets each (n = 15,000) using Twitter API. Results are interpreted using NRC emotion lexicon and data visualisation using R.

This study attempts to understand the public sentiment on three globally acclaimed Indian heritage sites, i.e. the Taj Mahal, Red Fort and Golden temple using a step-by-step approach, hence proposing a framework using Twitter analytics. Extensive use of various packages of R programming from the libraries has been done for various purposes such as extraction, processing and analysing the data from Twitter. A total of 15,000 tweets from January 2015 to January 2021 were collected of the three sites using different key words. An exploratory design and data visualisation technique has been used to interpret results.

After data processing, 12,409 sentiments are extracted. Amongst the three tourists' spots, the greatest number of positive sentiments is for the Taj Mahal and Golden temple with approximately 25% each. While the most negative sentiment can be seen for the Red Fort (17%). Amongst the positive emotions, the maximum joy sentiment (12%) can be seen in the Golden Temple and trust (21%) in the Red Fort. In terms of negative emotions, fear (13%) can be seen in the Red fort. Overall, India's heritage sites have a positive sentiment (20%), which surpasses the negative sentiment (13%). And can be said that the overall polarity is towards positive.

This study provides a framework on how to use Twitter for tourism insights through text mining public sentiments and provides real- time insights from famous Indian heritage sites.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

The purpose of this paper is to provide an early assessment of India’s South-South cooperation for trade and technology (SSTT) with East Africa, focusing on Ethiopia, Rwanda, Kenya, Uganda and Tanzania. It aims to analyse the role of SSTT in providing support to targeted sectors.

The paper examines SSTT, focusing on India and East Africa over a specific period (2000–2016) of its emergence, and extends the public sponsorship literature in international business (IB) to better understand the relationship between SSTT and value addition – applying to a particular case study of SSTT interventions in spices.

The paper highlights SSTT as a pathway to support value addition in global value chains (GVCs). Trade between India and East African countries has grown, with three developments over the period of analysis in particular: shifting trade patterns, growing share of intermediate goods trade and differences in GVC insertion. However, East African exports are largely of lower value. Capacity building to support processing capability and thriving markets can encourage greater value addition. Preliminary findings suggest early gains at the margins, as SSTT interventions have been focusing on capacity boosting with buffering and bridging mechanisms for increased volume of trade. Moving up the value chain however requires that specific value-enhancing activities continue to be targeted, building on regional capacities. Our high-level case study for spices suggests that activities are starting to have a positive effect; however, more focus is needed to specifically target value creation before export and in particular higher levels of processing.

While findings are preliminary, policy implications emerge to guide SSTT interventions. There is capacity for building higher value-added supply chains as is evident among East African countries that trade with each other – future SSTT programmes could tap into this and help build capacity in these higher-value value chains. Future SSTT programmes can take a comprehensive approach by aiming at interventions at key points of the value chain, and especially at points that facilitate higher value addition than initial processing. An example is that Ethiopia and Rwanda are likely to benefit from an expanded spice industry, but the next phase should be towards building processing for value-addition components of the value chain, such as through trade policies, incentivising exporters to add value to items before export. From a development perspective, more analysis needs to be done on the value chain itself – for instance, trade facilitation measures to help processers engage in value chains and to access investments for increasing value add activities. (iv), Future research should examine more closely the development impacts of SSTT, namely, the connection between increased trade, local job creation and sustained innovation, as it is these tangible benefits that will help countries in the Global South realise the benefits of increased trade.

The paper underlines how the SSTT approach can contribute to the critical IB and GVCs literature using a theoretical grounded approach from public sponsorship theory, and with a unique lens of development cooperation between countries in the global south and its emerging impact on development outcomes in these countries.