In today’s digital world, automated sentiment analysis from online reviews can contribute to a wide variety of decision-making processes. One example is examining typical perceptions of a product based on customer feedbacks to have a better understanding of consumer expectations, which can help enhance everything from customer service to product offerings. Online review comments, on the other hand, frequently mix different languages, use non-native scripts and do not adhere to strict grammar norms. For a low-resource language like Bangla, the lack of annotated code-mixed data makes automated sentiment analysis more challenging. To address this, we collect online reviews of different products and construct an annotated Bangla-English code mix (BE-CM) dataset ^a . On our sentiment corpus, we also compare several alternative models from the existing literature. We present a simple but effective data augmentation method that can be utilized with existing word embedding algorithms without the need for a parallel corpus to improve cross-lingual contextual understanding. Our experimental results suggest that training word embedding models (e.g., Word2vec, FastText) with our data augmentation strategy can help the model in capturing the cross-lingual relationship for code-mixed sentences, thereby improving the overall performance of existing classifiers in both supervised learning and zero-shot cross-lingual adaptability. With extensive experimentations, we found that XGBoost with Fasttext embedding trained on our proposed data augmentation method outperforms other alternative models in automated sentiment analysis on code-mixed Bangla-English dataset, with a weighted F1 score of 87%.

In this study, we examine the association between interim financing and firm performance in an emerging economy. Prior research shows that firms utilize trade credit to boost their operating performance or market valuation. However, recent research on the relation between trade credit as alternative financing and firm performance provides mixed evidence. Nevertheless, limited research has been conducted in developing economies; hence, we attempt to fill this gap in the present paper. We argue that trade credit may not be attractive to external debt financing as trade credit may not contribute to business growth while external debt financing does. To test our conjecture, we employed ordinary least squares (OLS), firm fixed effects, and random effects regressions. By utilizing 1002 firm-year observations, our findings suggest a negative relationship between trade credit and firm performance. To check and control endogeneity and reverse causality issues we use instrumental variable approach (i.e., Heckman two-stage least squares regression). Our results remain robust through different measures of firm performance and trade credit. Our study provides policy implications and contributions to trade credit and firm performance literature.

This study explores whether the family firms outperform nonfamily firms in Bangladesh. Secondly, we test whether family firms with political connections outperform non-connected family firms, given that more than 60 percent of Members of Parliament (MPs) have business backgrounds in the same setting. We find that the performance of nonfamily firms is significantly higher than that of family firms. However, we do not find any impact of political connection on the link between family ownership and market-based performance. At the same time, we document a negative impact of political connection on operating performance. Finally, our analysis shows that COVID-19 has negatively affected operating performance but positively affected market valuation, regardless of the firms' category. The paper’s findings will be highly important to researchers, policymakers, and academics in emerging economies.