The 'golden method' for scoring and sentiment analysis, which involves averaging multiple scores, can often be effective for low-resource and complex languages such as Bengali, Hindi and Arabic, as well as others. This approach can deliver on the challenges that arise when working in low-resource languages, such as the lack of complex natural language processing tools and the limited availability of labeled data. By using multiple annotators and averaging their labels, the 'golden method' can help reduce the impact of individual annotation biases and errors, and can provide a more reliable and consistent sentiment classification for a text. This approach is often used in research studies and industry applications to improve the accuracy of sentiment analysis models especially in low resource languages such as Arabic and Hindi. However, it is important to note that the effectiveness of this approach can depend on the size of the data set, the quality of the individual commentators, and the complexity of the language being analyzed. In some cases, additional techniques such as transfer learning or active learning may be required to further improve the accuracy of the sentiment analysis model for low-resource languages such as Arabic, Hindi, and others.

I think a new parameter should be added to the golden method. That is the annotators must be neutral. Because I have seen so many taggings where the avg sentiment is refering to sad or boring but to me it feels like angry or awful.

I know it might be my bias. But their bias can also be in effect. Because low resource and complex language strongly depends on interpretation. Which is not the case for english.

Do you agree?? I think Golden method helps but only a little.

As a proponent of the "golden method", which essentially averages sentiment scores from various taggers, I believe it has potential for numerous languages, even ones like Bangla and Hindi that may have less linguistic resources available. However, its efficacy largely rests on the competence and quantity of the individual sentiment taggers that constitute the ensemble.

When it comes to languages with fewer resources and greater complexities, a couple of challenges emerge:

1. Lack of annotated data: To build a precise sentiment tagger, one often needs ample annotated data. Such data might be scarce for less-resourced languages.

2. Intricate linguistic elements: Languages such as Bangla and Hindi come with complex grammar and a wide array of morphological variations, adding difficulty to sentiment analysis.

Despite these hurdles, the ensemble method can still prove advantageous. By blending diverse taggers, potential errors from any individual tagger can be mitigated, leading to a potential boost in overall performance. However, it's essential for me to note that for these languages, individual taggers should be fine-tuned and trained on pertinent data.

To wrap up, the "golden method" can offer certain advantages but it's not a silver bullet for less-resourced and intricate languages. For the best results, I'd recommend integrating it with other tactics such as transfer learning, creating language-specific resources, or utilizing multilingual models.