Amharic Negation Handling
Authors: Girma Neshir
Venue:  (2020)
Keywords: Negation Handling Algorithm
Amharic Sentiment Analysis
Amharic Sentiment lexicon
char level
word level ngram
machine learning
hybrid

Abstract: 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).

Under review as a conference paper at ICLR 2020
NEGATION HANDLING FOR AMHARIC SENTIMENT CLASSIFI- CATION
Anonymous authors Paper under double-blind review
ABSTRACT
User generated content contains opinionated texts not only in dominant languages (like English) but also less dominant languages( like Amharic). However, nega- tion handling techniques that supports for sentiment detection is not developed in such less dominant languago(i.e. Amharic). Negation handling is one of the chal- lenging tasks for sentiment classification. Thus, this work builds negation han- dling schemes which enhances Amharic Sentiment classification. 'he 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). keywords:Negation Handling Algorithm, Amharic Sentiment Analysis, Amharic Sentiment lexicon, char level, word level ngram, machine learning, hybrid
INTRODUCTION
Due to the emergence social media including Facebook, Twitter, YouTube, Insta-grams, LinkedIn and on, the number of users who participates and consumes in-formation is increasing very fast. Users usually express their feelings, emotions and opinions as comments in response to the posted news, photo, audio and video. The texts in social media are informal it contains spelling errors, slangs, abbreviations or users might use different language. Preprocessing these unstructured and informal texts is very challenging prior to sentiment analysis. Preprocessing includes tokeniza-tion, punctuation mark removal, stop word removal, abbreviation expansion, language detection, spelling error handling, normalization and stemming. The preprocessing task could be harder for under re- source languages (e.g. Amharic). Analyss is the process of framing the unstructured texts to detect, extract and classify opinion words. Currently, opinionated sources are increasing in lan- guages other than English. Amharic is one of these lesource-limited languages. However, Amharic sentiment analysis researches are very few as has no sufficient linguistic resources for linguis- tic preprocessing and sentiment analysis. There are several challenges in lexicon based sentiment analysis. One of these is that handling negation in the text. The most common approach for nega- tion handling is carried out relying on negation keywords. However, it is challenging to identify the scope of negation where the process of correctly identifying the part of the text affected by the presence of negation word. In most negation handling researches, simple approach of detection of negation scope is used. Amharic is one of the most spoken Semitic languages in the world next to Arabic. Amharic is morphologically rich and this adds another challenge to detect and extract opin- ion words in sentiment analysis. Inspired by the aforementioned preprocessing problems, negation handling is never studied in Amharic language to the best of our knowledge. Thus, the main aim of this research is develop an automatic method to handle negation and combined with char ngram features for Amharic Sentiment Analysis relying on these linguistic features. The research questions to be addressed in this work are as follows: (a) how can we automatically detect negation words in Amharic texts?, (b) how can we design framework for handling negation in Amharic sentiment analysis?, (c) how to capture char level ngram features for improving Amharic Sentiment Analysis in Social media(e..g. g. Facebook) and (d) how can we evaluate the performance of the framework? The rest of this paper is organized follows: in the section 2, the related works presented. The pro- posed approaches including negation handling and capturing char level ngram features for Amharic Sentiment Analysis is described in section 3. In section 4, results and discussions are presented.

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Under review as a conference paper at ICLR 2020
Conclusions and recommendations drawn in section 5. In this section, briefly present the key related works. Asmi & Ishaya (2012) proposed an approach that can detect negation and con- siders scope of negation relying syntactic dependency for sentiment analysis. In (Amalia al.) 2018), proposed rule based negation handling and its scope based on syntactic parsing of Indonesian language for machine learning based sentiment analysis in twitter and the result of support vector machine performs well as compared with other experiments. The F-Score on two Twitter data sets improved by 1.79% and 2.69% from the existing baseline without negation handling. In (Faroog et al. 2017). develop negation scope handling strategies relying on the linguistic features of the lan- guage. The accuracy of negation scope identification is 83.3% which outperforms very well above the baseline. In (Diamantini et al. 20166,, develops negation scope detection relying on dependency parsing tree and semantic disambiguation technique and evaluated on integrated social networks in real time. The performance of negation handling is outperforming with accuracy of 6% more than the bascline. The work in (Heerschop et al. 2011) develops negation handling relying on wordbank creation and document sentiment scoring for sentiment analysis and outperforms the human rating by an increase precision with 1.17% 1.1% In (Enger et al., 2017), developed negation cue and scope handling open source tool relying dependency parser, negation cue(negation lists, prefix, suffix) as input to machine learning(e.g, SVM) and its performance is slightly unchanged from the baseline.
2
METHODS
2.1
DATA SETS LEXICAL RESOURCES
This subsection describes the main data sets and lexical resources used for building and evaluating the proposed framework.
2.2
FACEBOOK ANNOTATED AMHARIC NEWS COMMENTS
This dataset consists of 2705 sentence/phrase level sentiment annotated facebook users' com- ments collected from the Government Office A Affairs Communication (GOAC) between 2008 and 2010. News that received high view counters frequent comments were selected "hot topics" and the associated comments labeled by professionals into either positive or negative sentiment. The data sets for evaluating the performance of the negation handling and char level ngram based model for Amharic sentiment analysis relying on sentence level sentiment scoring.
2.3
AMHARIC SENTIMENT LEXICONS
The Amharic sentiment
includes manual 1000) SWN(13679) (Neshir Alemneh al. 2019) and SOCAL(5683) (Neshir Alemneh et al.) 2019
lexicons
(Gebremeskel
2010
2.4
PROPOSED APPROACHES
The proposed framework includes the algorithm for negation handling and the sentiment scoring strategies. The framework for negation handling and char level ngram based model is shown in Figure below.
Antaric News
Char Level Ngram based ifersifers (NE+LR
Comments
Amharic Sentiment Lexcons
Preprocessing
Negation Handling Agorithm
Sentiment Score Calbulation
Sentiment class
Figure 1: Proposed Framework for Amharic Sentiment Classification
The stages in the framework are briefly described as follows:
2

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Under review as a conference paper at ICLR 2020
2.5 PREPROCESSING
We apply basic preprocessing on Amharic News Comments. These include normalization of Amharic script symbols, tokenization, stop word removal, punctuation mark removal and Amharic writing system expressed using only consonants. To handle the features of the language very challenging. We require conversion of Amharic seripts to consonant-vowel form. Partieularly, be- fore performing negation handling and stemming, the algorithm converts each Amharic word to its consonant vowel form.
2.6 NEGATION DETECTION
We developed negation detection algorithm which re-turns true if word contains negation either prefix( ΛAXPe,, oY7A, AAE, dh,e, oXA, oX9, oX7, o'e, LhA, AA? dx7, AAA, hhg)) suffix (HKhh, ęāлXT) or a word in negation lists. Before word is checked for negation clue, the Amharic word should be converted to consonant vowel form. The aforementioned Amharic prefixes and suffixes indicating negations which are written in consonant- vowel form. Figure 2 represents the finite state automata of Amharic morphological negation clues indicated by using prefix, suffix or negation carrying word. An Amharic word is given as input to this finite state automaton and the finite state automata (FSA) process the patterns of the word relying on the regular expressions. If the word contains either of the prefixes, it will finish processing on the state containing stem. it contains one of the suffixes, processing on the finish state if it contains neither prefix nor suffix, it will finish on the start state. And then check the word is in the negation list(NL). If any of these is satisfied, it will return true, otherwise returns false.
will finish
NL
Negation Liet
Start
Prefix
Stem
Sufxx
Finish
Figure State Automata for Amharic Morphological Negation Clues
Assume the jth Amharic news comment, C, in Amharic News Comments collection, da, has preprocessed and finally, the comment is tokenized into lists. That is, C Wig.:,,....,,,,j.... WIN, ), where N the number of tokens in 7th comments, C. Let the Amharic Sentiment Lexicon is denoted by S., As part of preprocessing, we normalized not only all Amharic words in the Amharic News Comments but also handling entries of Amharic Sentiment Lexicon by replacing varied alphabets of the same sound with identical symbols. Moreover, stemmer is applied for after negation identification is completed. As Amharic is morphologically rich, reduce the mismatch of Amharic words during string comparison operation, we used stemming for this purpose. Below is the Negation handling algorithm in 1
3

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Under review as a conference paper at ICLR 2020
Input: wordorginat: input word
Output: Is nagation: returns True if there is negation clue, otherwise false
sufix+ sufix+rr(:*") (ANA (n (* prefixt " F(ohAXr"X |aaXrrr -o -ihee Sr |oXA| (XX |ox | |
word CV forme to CVWwoodddoiggnnee
prefix truegetsFalse suffix truegetaFalse
len(word)>3: then stem, suffix gets re.findallneegatinn _utiiiss__uufuuueee word CV word_CV__oonee
CVrfoMMO wordgets stem
if len(suffix)=1: then suffix truegetsTrue try: stem,prefix gets re.findalllnegation preffix stem)[0][1), re.find-
all(negation prrepattoonprefiix _   stem)[0] wordgets stem llen(prefix)=/: then prefix truegets True except: pass
prefix mue: then Return prefix_ true;
if suffix true: then Retrun suffix true
n suffix true is False and prefix true is False then 12 Return False
algorithm 1: Amharic Negation Handling Algorithm
2.7 SENTIMENT SCORE CALCULATION
comment, C,, if each stemmed word wij is found in either of the Amharic Sentiment lexicons (Manual, SOCAL, SWN), then the sentiment score Sij is retrieved. Sij and its position index in the comment stored. To compute the sentiment of the comment, we apply positional weighting inversion if the comment contains any negation clue. negation clue is not found, the score of the word is simply added. The sentiment Score computing algorithm is presented in Input: Inpul: The in Amharic News Comment, C, of the comment Collection.
For each Amharic
Output: Output: SentimentClass(citeer positive, negative or ncutral/mixed)
13 S.+ Load Amharic Sentiment Lexicons from file Initialize parameters: Scoreynder + Sentigcore =
0, N.Negttion = 0 C: + [wil, wi2, ..., wij, .... wiN] = preprocess(C:), tokenize, normalize the com- ment
valuegets False
15 foreach way C. do
16
Wey in S. then
17
Score Score_Index/e Index[i] + [wig]
DetectNeeattion((n..)) then Negation+-j
19
L
29
if Negation is not zero: then
foreach k Score Index.keys: do
23
i Negation and Negation not Zero: then
23
Senti Score=k* Senti Index[k] Sum=Sum-k
if 1> Negation and Negation not Zero: then
N=len(Senti Index.keys) Senti Score=Ngaatioon Senti [ndex]]] Sum=Sum+(N-k)
if Negation N=len(Senti Index.keys() Senti Score=sum(Senti Index, Values) if (Sum>>0: 0: then Senti Score-Sent: Senti Score/Sum
then
27
28
S
30 if Senti Score>0: then Return "Positive"
32 SentiScore=0; then
33
Return "Negative"
SentiScore==0: then
38 Return "Nuural//ixc//Unccassiiae
algorithm 2: Amharic Sentiment Score Calculation Algorithm

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Under review as a conference paper at ICLR 2020
As specified in algorithm 2 the sentiment class of the comment is decided based the value of the computed sentiment score. If the score is greater than zero, then the sentiment of the comment is positive. If the score is less than zero, the sentiment of the comment is negative. Otherwise, the sentiment of the comment is unclassified or neutral mixed.
2.8 CHAR LEVEL NGRAMS FOR AMHARIC SENTIMENT ANALYSIS
In this stage, we propose machine learning based Amharic Sentiment classification that takes the character level ngrams tfidf features of Amharic text. Yet, Amharic language lacks standard linguistic specific tools including dependency parser and part of speech tagger to capture the scope of negation in Amharic text for sentiment Analysis. Besides lexicon based negation handling approach, we try to enhance the performance of Amharic sentiment Analysis relying on char level ngram tfidf feature weights. As we hoped that there is flexibility capture language dependent features using character level ngram models. The usefulness of character aware language models well suited to apply for language identification, reducing of text feature sparse dimensionality, helps to handle spelling errors, abbreviations, special characters and so on [7]. These problems are particularly found in texts of social media where texts data are too noisy and informal in nature to be processed using standard natural language processing toolkit. That is why we propose character level ngram approaches to reduce and address these issues for Amharic facebook news comments' sentiment analysis rather than word level ngram approaches. For example, the negation carrying Amharic word "hAmgarre" "I donot like him"/ has 2-gram character level features includes: h-ha-A0-08-R0----9-
3-gram character level features are h- ha-AAm- and so on. In both cases, the negation marker/morpheme// hA- is detected as feature of the negation the negation word in Amharic text relying on character level ngram features and in turn its scope will also be captured in the same manner from its surrounding character ngram features that are assumed to enhance the performance of sentiment analysis. For each Amharic facebook news comment, term irequency-invers document frequency (tfidf) weighted char level ngrams(e.g. bigrams and trigrams) features are extracted and becomes input to ma-chine learning classifiers (e.g. logistic regression and Naïve Bayesian) for training and validation. Then, the two machine learning algorithms are ensembled with lexicon based negation handling algorithm to predict the sentiment class of Amharic comments. The usefulness of the combined (hybrid) framework is measured using testing data set partitioned from annotated Amharic news comments. The framework is implemented using python scikit learn library and the result and discussion are presented in the following subsection.
Thus, we can identify
3 RESULTS AND DISCUSSIONS
In this section, we will evaluate the usefulness of the combination of Negation Han- dling(NH) and character level ngram based models for sentiment classification of Amharic facebook news comments. We call the combination of rule based NH and char ngram based models as hybrid model for Amharic Sentiment classification. In nega- tion handling approach, Amharic texts require basic text preprocessing (tokenization, punctuation mark removal, normalizing Amharic script symbols, stopword removal, spelling corrector, stemming, etc.). The effect of stemming and negation detection technique on Amharic text is investigated to increase the accuracy of lexicon based Amharic sentiment classification. However, to extract char level ngram features, pre- processing tools including stemming, removal of punctuation marks, removal stop words, abbreviation expansion, etc. are not required. Thus, prior to char ngram based sentiment classification, we partition the Annotated Amharic Facebook News Comment corpus into training and testing sets. Logistic regression(LR) and Naïve Bayesian(NB) models are built relying on the char level bi-gram and tri-gram features of training set. For Amharic Sentiment Classification, the accuracy of the combined models on the test set are presented in Table 1
5

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Under review as a conference paper at ICLR 2020
Table 1: The Accuracy (in percent) of Amharic Sentiment Classification Models
Accuracy(%)
Approach
Wordlevel (Baseline)
Char Level
Negation Handling(NH)
86.2
NB + LR
79.32
83.75
(H
95.27
98.0
Discussions: In Table [] the usefulness of the proposed approach is tested relying on their accuracies of classifying sentiment of Amharic facebook news comments. The results in Table [ show that negation handling algorithm outperforms very well (acc. 86.2%) than the performance of character level and word level based machine learning models for classifying sentiment of Amharic texts. On the other hand, character level ngram based classifier is more useful for classifying Amharic Sentiment than word level ngram models (baseline). Finally, the hybrid model is obtained by combining negation handling approach and char ngram models (NB+ + LR). This hybrid model outperforms with accuracy of 98% than the other models and its combinations. Yet, it is quite difficult to find why errors are generated in predicting sentiment category of Amharic Facebook News Comment Text. On the basis of on-spot detection, the associated reasons for most of wrongly predicted Amharic News Comments are presented as follows: (a) we detect errors coming from professionals who annotated Amharic News comments. For example, Po76477 na R.RLLC .Λ- p4.47 21mm%% GTFm= '/ESAT journalists are making the country be disturbed/. The human annotator assigned positive sentiment label to this comment. It should rather be assigned negative label. To this end, the data should be revisited for correction. (b) we detect some comments contains opinion words but the sentiment orientation of the whole comment is opposite to the polarity of the opinion words in the comment. The reason for this is that the comment is conditional sentence. It talks about hope rather than opinion. For example, the Amharic news comments 'A PV" YIC œni 0.7-A Tς. 'a>: will be good you tell the truth/. In this comment, there are two positive words (e-g. good and truth). However, the interpretation of the whole comment is talking about that they told us fake news. Similarly, we detect some comments represents wishes that need to be done. For example, 'N p"1,17 neT2 N--MC 7786600mm77 X76.A,077 '/We need to see accomplished in practice that we heard in words/. This comment has no any opinion words which helps the proposed approach to predict the sentiment of the comment correctly. Further researches needs to carry out to reduce the source of errors in predicting sentiment class of Amharic comments. Our recent findings is a good starting point to improve the performance news comments. Fine tuning char ngram features shows suitedness and flexible for sentiment analysis of resource limited language (e.g. Amharic) than word level ngram models.
Amharic sentiment analysis in facebook
CONCLUSIONS
In general, extensive linguistic resources are expensive to build sentiment analysis on the less dominant languages (e.g. Amharic). To reduce this problem, we propose negation handling approach and char ngram approach for Sentiment analysis of Amharic facebook news comments. The proposed approaches are evaluated by measuring accuracy of individual and their combinations for Amharic text sentiment classification. This work reveals that combining rule based (negation handling approach) with machine learning approach (char level ngram feature models) outperformed the best of the individual approaches. This enhances performance of sentiment classification on Amharic news comments on social media (e.g. Facebook). Some of the contributions of this work are briefly summarized below:
The proposed negation handling approach for Amharic Sentiment Analysis out- performs very well when we compared to the char ngram based machine learning classifiers.
The approach for negation handling can be adapted to develop Amharic text stemmer algorithm.

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Under review as a conference paper at ICLR 2020
We developed an approach that can be adapted to sentiment analysis of other resource limited languages.
The char ngram based machine learning is promising that it reduces the demand for linguistic resources for less dominant languages.
The approach also be adapted to other tasks of natural language processing including language identification, topic based text categorization, authorship attributions, just to name few.
The hybrid approach (NH + NB+ outperforms the best compared to the individual approach for sentiment classification on Amharic facebook news comment texts.
The code and related resources will be accessible publicly for research com- munities.
So far, we have seen that the proposed approach still lacks accuracy for sentiment analysis of a specific language (e.g. Amharic). The approach potentially does not sufficiently capture the language specific features that help to identify the sentiment class of Amharic news comment text in social media. Further work should be per- formed to reduce the amount of errors sentiment analysis of Amharic facebook news comments. To address these issues, we may need to consider char ngram embedding features from corpus of the same domain(e.g. Facebook news comments).
REFERENCES
Rizkiana Amalia, Moch Arif Bijaksana, and Dhinta Darmantoro. Negation handling in sentiment classification using rule-based adapted from indonesian language syntactic for indonesian text in twitter. In Journal of Physics: Conference Series, volume 971,
pp. 012039. IOP Publishing, 2018.
Amna Asmi and Tanko Ishaya. Negation identification and calculation in sentiment analysis. In The second international conference on advances in information mining and management, pp. 1--7, 2012.
Claudia Diamantini, Alex Mircoli, and Domenico Potena. A negation handling tech- nique for sentiment analysis. In 2016 International Conference on Collaboration Technologies and Systems (CTS), pp. 188--195. IEEE, 2016.
Martine Enger, Erik Velldal, and Lilja Øvrelid. An open-source tool for negation detec- tion: a maximum-margin approach. In Proceedings of the Workshop Computational Semantics Beyond Events and Roles, pp. 64--69, 2017.
Umar Farooq, Hasan Mansoor, Antoine Nongaillard, Yacine Ouzrout, and Muham- mad Abdul Qadir. Negation handling in sentiment analysis at sentence level. JCP, 12(5):470-478, 2017.
S. Gebremeskel. Sentiment mining model for opinionated amharic texts. Unpublished Masters Thesis and Department of Computer Science and Addis Ababa University and Addis Ababa, 2010.
Bas Heerschop, Paul van Iterson, Alexander Hogenboom, Flavius Frasincar, and Uzay Kaymak. Accounting for negation in sentiment analysis. In 11th Dutch-Belgin Information Retrieval Workshop (DIR 2011), pp. 38--39, 2011.
Girma Neshir Alemneh, Andreas Rauber, and Solomon Atnafu. Dictionary Based Amharic Sentiment Lexicon Generation, pp. 311--326. 08 2019. ISBN 978-3-030-
26629-5, doi:


Reviews:
[{"review_id": "SyetO1uNqB", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "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.\n\nMy 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.\n", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "SJlfBvGgjB", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "While this paper tackles an interesting problem. The technical approach is unfortunately too outdated and obvious and not quite the level of ICLR. \nThe dataset is likely too easy given the high accuracy. \n                                                                                                                                                                                                                                                                                                          ", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I made a quick assessment of this paper.", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A"}, "extra_text": {}}, {"review_id": "B1lddR7l5S", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper is quite difficult to read. The figures are pixelated. There is almost no organization to the text of the work.\n\nDescriptions are imprecise and lax: for example, \"We apply basic preprocessing on Amharic News Comments. These include normalization of Amharic script symbols, tokenization, stop word removal, punctuation mark removal and so on. Amharic writing system is expressed using only consonants. To handle the features of the language is very challenging. We require conversion of Amharic scripts to consonant-vowel form. Particularly, before performing negation handling and stemming, the algorithm converts each Amharic word to its consonant vowel form.\" What kind of normalization? What kind of tokenization? Did you use outside tools? If so, cite them or refer to the code. If not, then please explain your method more thoroughly. Don't assume all your readers know what consonant-vowel form is. \"and so on\" is not appropriate. You should explain what the so on is in a paper submitted to a conference. Don't bother telling us the features of the language are challenging. Just tell us how you did it.\n\nSwitching between words like \"cue\" and \"clue\"\n\nEven the font of the text changes part way through the paper.\n\nFigure 2 is almost entirely uninformative.\n\nThe algorithms are poorly displayed and nearly unreadable.\n\nExperiments are too limited.", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_thoroughness_in_paper_reading": "N/A", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "N/A"}, "extra_text": {}}]

Author rebuttal:

Meta-review: Main content:

This paper presents negation handling approaches for Amharic sentiment classification.

--

Discussion:

All reviewers agree the paper is poorly written, uses outdated approaches, and requires better organization and formatting.

--

Recommendation and justification:

This paper after more work might be better submitted in an NLP workshop on low resource languages, rather than ICLR which is more focused on new machine learning methods.
Decision: Reject