Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews. To employ rule based sentiment classification, we require sentiment lexicons. However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages. To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach. The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus. Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb. We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus. Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI). Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds. Finally, the lexicon generated in corpus based approach is evaluated.
This paper proposes a domain-specific corpus-based approach for generating semantic lexicons for the low-resource Amharic language. Manual construction of lexicons is especially hard and expensive for low-resource languages. More importantly, the paper points out that existing dictionaries and lexicons do not capture cultural connotations and language specific features, which is rather important for tasks like sentiment classification. Instead, this work proposes to automatically generate a semantic lexicon using distributional semantics from a corpus.
The proposed approach starts from a seed list of sentiment words in 3 pre-determined POS classes. This is followed by deriving a PPMI matrix from the word-context co-occurence matrix (context size= 2). Now given a word, the cosine distance is computed from centroid of each seed classes and words which are similar than a given threshold, are added to the original list. This process is repeated for a pre-specified number of iterations. They apply the generated lexicons to subjectivity detection and sentiment classification task in a small annotated Amharic corpus of facebook posts
Strengths:
1. NLP tasks on low-resource language is very challenging. This paper proposes a efficient, unsupervised way of gathering semantic lexicons that perform reasonably well on a downstream task.
Weaknesses:
1. Related work: The paper needs to cite and mention a much more broader literature. Currently, it mentions just 3 related work, and contrasts itself with one. But given there is so much work on distributional semantics (in English and other languages), they deserve mentioning. Few example of ones are 1. The distributional inclusion hypotheses and lexical entailment (Geffet and Dagan 2005), Distributed representations of words and phrases and their compositionality (Mikolov et al., 2013), Improving hypernymy detection with an integrated path-based and distributional method (Shwartz et al., 2017), Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection (Chang et al., 2018). However, this is by no means a complete list and you should refer to these papers to get a list of other relevant work.
2. Novelty of work: The novelty of the work is rather limited and the paper should try more low-dimensional embedding based approaches which has been proven to be very effective for a wide variety of tasks. In section 3, the paper mentions there are primarily two kinds of approaches (count based vs embedding based) — The paper should motivate why it chose one over another.
3. Baselines: The paper would benefit from having some comparison with learned baselines trained from some distantly supervised data from, for example the SWN and SOCAL lexicons. 
4. Organization of the paper: The writing and the organization of the paper needs to be better. For example, equation 3, 4, 5 are rather simple and can be condensed to one equation (no need to show averaging of the seed embeddings). Also in equation 3, would \vec{w} would be \vec{x}?. The results and discussions did not have either and over-all there are many grammatical errors. 
5. I am not sure what the subjectivity detection task is in Table 3. Is it a standard task -- if not, the paper should define it first.
Overall, the paper is a nice effort but in its current form it is not ready for ICLR and I hope the comments will help make the paper better for upcoming NLP workshops and conferences.