LPS @ LT - EDI - ACL 2022:An Ensemble Approach about Hope Speech Detection (2022)

Abstract: The task shared by sponsor about Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI-ACL-2022.The goal of this task is to identify whether a given comment contains hope speech or not,and hope is considered significant for the well-being, recuperation and restoration of human life. Our work aims to change the prevalent way of thinking by moving away from a preoccupation with discrimination, loneliness or the worst things in life to building the confidence, support and good qualities based on comments by individuals. In response to the need to detect equality, diversity and inclusion of hope speech in a multilingual environment, we built an integration model and achieved well performance on multiple datasets presented by the sponsor and the specific results can be referred to the experimental results section.

## introduction in the age of multimedia information technology, massive network data is a symbol of people's freedom of speech,and these messages contain a lot of positive or negative sentiments.past research has mostly focused on sentiment analysis, or negative detection of insults,aggression and hate speech 1 @xcite @xcite @xcite @xcite . instead,the goal of this task @xcite shared at lt-edi 2022-acl 2022 2 is to determine whether a given comment contains hope speech or not in tamil, malayalam, kannada, english and spanish. tamil, malayalam, and kannada belongs to dravidian languages @xcite @xcite . tamil is an official language of the indian 1 https://competitions.codalab.org/ competitions/25295 2 https://competitions.codalab.org/ competitions/36393 state of tamil nadu, the sovereign nations of sri lanka and singapore, and the union territory of puducherry @xcite @xcite @xcite . the dravidian languages are first attested in the 6th century bce as tamili (also called tamil-brahmi) script inscribed on the cave walls in the madurai and tirunelveli districts of tamil nadu @xcite . research should take a positive reinforcement approach.the aim is to change the prevailing mindset by moving away from focusing on discrimination,loneliness or the worst things in life to building confidence,support and good character based on personal comments @xcite .therefore,we built an ensemble model to detect user-generated comment sentences from the social media platform (youtube) that contained hope speech or not, and our model achieves good results on relevant data sets foot_0 .this is a study of a speech of hope that interprets equality, diversity and inclusion in a multilingual environment. we have open-sourced our code implementations on github foot_1 . ## related work the study found that in the past, people mainly focused on the sentiment analysis of monolingual (english) @xcite ,or the negative detection of insult, attack and hate speech in mixed or multilingual languages.while there were few studies on the hope speech detection of equality, diversity and inclusion in multilingual environments(as @xcite ). in particular, studies that use positive reinforcement methods to build people's confidence, support and data class english spanish kannada malayalam tamil training non_hope_speech 20778 499 3241 6205 7872 hope_speech 1962 491 1699 1668 6327 development non_hope_speech 2569 161 408 784 998 hope_speech 272 169 213 190 757 test 2843 330 618 1071 1761 total 28424 1650 6176 9918 17715 ## dataset the dataset(chakravarthi, 2020b) is provided by acl 2022 contains 59,354 comments from the famous online video sharing platform youtube out of which 28,424 are in english, 1,650 in spanish, 6,176 in kannada @xcite , 9,918 in malayalam, and 17,715 comments are in tamil ( it is observed that the sentence of data is in a 5 https://drive.google.com/file/d/ 1uoxyblvucofaofuw56kjklx-t_nl4mlf/view code-mixed format (a mixture of native type and roman type), and contains a lot of @ names, repeated words or letters, useless symbols, expressions, etc.before feeding the raw tweets to any training stage,we will do a simple data preprocessing. ## model framework and experimental results this section introduces the structure of our model and experimental results. ## conclusion the ensemble model our submitted consisted of three parts:lstm,cnn+lstm and bilstm. among them,cnn,to some extent,takes into account the ordering of the words and the context in which each word appears. using the lstm model can better capture long distance dependencies. because lstm can learn what to remember and what to forget through the training process,but lstm doesn't take into account the sequential order of words in a sentence.lstm has problems with ambiguous affective words in finer -grained classification. therefore, bilstm can better capture the bidirectional semantic dependencies, taking into account the reverse information.finally, on this basis, attention mechanism is introduced to highlight the key information. in other words, by adjusting a series of weight parameters, it can be used to emphasize or select the important information of the target processing object and suppress some irrelevant details, so as to make the classification more accurate.the model we submitted has achieved performance well, but there is still a lot of room for improvement in both pre-processing and model framework design in the future.