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Students' feedback is crucial for academic institutions in order to evaluate faculty performance. Handling the qualitative opinions of students efficiently while automatic report generation is a challenging task. Indeed, most organizations deal with quantitative feedback effectively, whereas qualitative feedback is either processed manually or ignored altogether. This study proposes a supervised aspect based opinion mining system based on two-layered LSTM model. The first layer predicts the aspects described within the feedback and later specifies the orientation (positive, negative, and neutral) of those predicted aspects. The model was tested on a manually tagged data set constructed from the last five years students' comments from Sukkur IBA University as well as on a standard SemEval-2014 data set. Unlike many other LSTM models proposed for other domains, the proposed model is quite simple in terms of architecture which results in less complexity. The system attains a good accuracy using the domain embedding layer in both tasks: aspect extraction (91%) and sentiment polarity detection (93%). To the best of our knowledge, this study is a first attempt that uses deep learning approach for performing aspect based sentiment analysis on students' feedback for evaluating faculty teaching performance.

Purpose The purpose of this paper is to investigate the relationship between firms’ life cycle stages (mature vs growth) and green process innovation performance. In addition, this research delineates the mechanism by which the mature stage firms are more strongly associated with green process innovation performance compared to growth stage firms and recognizes technological capabilities as a mediating variable fundamental to achieve a higher level of green process innovation performance. Design/methodology/approach This research collected data from 202 publicly listed Thai manufacturing firms. Initially, it used multiple regression analysis to test the relationship between mature stage firms and green process innovation performance compared to the relationship between growth stage firms and green process innovation performance. Later, this research followed Muller et al. (2005) to test the mediating role of technological capabilities and conducted (Sobel, 1982, 1986; Preacher and Hayes, 2004) tests to further validate the mediation effect. Findings The hypothesized relationships were found to be significant, providing a strong support that mature stage firms have higher green process innovation performance compared with growth stage firms. Moreover, the technological capabilities more strongly mediate the relationship between mature stage firms and green process innovation performance compared to growth stage firms and green process innovation performance. Originality/value This research contributes to the existing understanding about the internal drivers of green process innovation performance by incorporating and analyzing the firms’ life cycle stages as an internal driver. This research also contributes by empirically testing the mediating role of technological capabilities on the relationship between firms’ life cycle stages and green process innovation performance.