Abstract.

Temporal information extraction from raw text is always challenging. It is time consuming and sometimes difficult to extract temporal expression manually. For this reason, an automatic system is a demand to find the temporal expressions from the textual data automatically. In this paper, we have developed a temporal information extraction system using Long Short Term Memory (LSTM) recurrent neural network (RNN) along with word embedding where temporal expressions are extracted from TempEval-2 dataset. Performance of the proposed LSTM RNN based system is highly comparable with the other entries of TempEval-2 challenge. As LSTM RNN can handle both long and short term dependencies, the proposed system shows robust result than other renowned existing systems.

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Abstract.

This study looks at factors that effect on consumers’ intentions to buy online, especially from Facebook. We enlighten the impact and analyze how factors influence consumers to purchase products from Facebook. Specifically, we observe consumer behaviors using different viewpoints. Some viewpoints are related to psychology, and some are relevant to the experiences of consumers. We emphasize the analysis of those intentions that work behind the consumption of any product from a Facebook page or group. An analytical study in which the contributions of all assumptions are investigated and reported. We gather the perceptions of 505 people regarding buying products from Facebook pages or groups. In terms of relative contributions, we find two models and evaluation matrices that indicate the accuracy of those models to predict the consumers’ purchases from Facebook pages or groups.