Amharic Negation Handling
User generated content contains opinionated texts not only in dominant languages (like English) but also less dominant languages( like Amharic). However, negation handling techniques that supports for sentiment detection is not developed in such less dominant language(i.e. Amharic). Negation handling is one of the challenging tasks for sentiment classification. Thus, this work builds negation handling schemes which enhances Amharic Sentiment classification. The proposed Negation Handling framework combines the lexicon based approach and character ngram based machine learning model. The performance of framework is evaluated using the annotated Amharic News Comments. The system is outperforming the best of all models and the baselines by an accuracy of 98.0. The result is compared with the baselines (without negation handling and word level ngram model).
This paper introduces a method combining the lexicon-based feature and character ngram model for handling negation in Amharic sentiment classification. The algorithmic contribution is demonstrated in a Amharic dataset. My rating for this paper is Reject because there is no novelty in the approach, it introduces some rules to build a FSA plus a logistic regression model with character-level ngram feature. It is also lack of argument/motivation why the proposed model is special and specific to the problem. The experiment only compares with two baselines in single dataset, which is also not convincing.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Amharic Negation Handling
User generated content contains opinionated texts not only in dominant languages (like English) but also less dominant languages( like Amharic). However, negation handling techniques that supports for sentiment detection is not developed in such less dominant language(i.e. Amharic). Negation handling is one of the challenging tasks for sentiment classification. Thus, this work builds negation handling schemes which enhances Amharic Sentiment classification. The proposed Negation Handling framework combines the lexicon based approach and character ngram based machine learning model. The performance of framework is evaluated using the annotated Amharic News Comments. The system is outperforming the best of all models and the baselines by an accuracy of 98.0. The result is compared with the baselines (without negation handling and word level ngram model).
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper introduces a method combining the lexicon-based feature and character ngram model for handling negation in Amharic sentiment classification. The algorithmic contribution is demonstrated in a Amharic dataset. My rating for this paper is Reject because there is no novelty in the approach, it introduces some rules to build a FSA plus a logistic regression model with character-level ngram feature. It is also lack of argument/motivation why the proposed model is special and specific to the problem. The experiment only compares with two baselines in single dataset, which is also not convincing.
assistant
This paper introduces a method combining the lexicon-based feature and character ngram model for handling negation in Amharic sentiment classification. The algorithmic contribution is demonstrated in a Amharic dataset. My rating for this paper is Reject because there is no novelty in the approach, it introduces some rules to build a FSA plus a logistic regression model with character-level ngram feature. It is also lack of argument/motivation why the proposed model is special and specific to the problem. The experiment only compares with two baselines in single dataset, which is also not convincing.