sentimentr | Dictionary based sentiment analysis that considers valence | Natural Language Processing library

sentimentr is a R library typically used in Artificial Intelligence, Natural Language Processing applications. sentimentr has no bugs, it has no vulnerabilities and it has low support. However sentimentr has a Non-SPDX License. You can download it from GitHub.

The equation below describes the augmented dictionary method of sentimentr that may give better results than a simple lookup dictionary approach that does not consider valence shifters. The equation used by the algorithm to assign value to polarity of each sentence fist utilizes a sentiment dictionary (e.g., Jockers, [(2017)] to tag polarized words. Each paragraph (p*i* = {s1, s2, …​, s*n*}) composed of sentences, is broken into element sentences (s*i*, j = {w1, w2, …​, w*n*}) where w are the words within sentences. Each sentence (s*j*) is broken into a an ordered bag of words. Punctuation is removed with the exception of pause punctuations (commas, colons, semicolons) which are considered a word within the sentence. I will denote pause words as c*w* (comma words) for convenience. We can represent these words as an i,j,k notation as w*i*, j, k. For example w3, 2, 5 would be the fifth word of the second sentence of the third paragraph. While I use the term paragraph this merely represent a complete turn of talk. For example it may be a cell level response in a questionnaire composed of sentences. The words in each sentence (w*i*, j, k) are searched and compared to a dictionary of polarized words (e.g., a combined and augmented version of Jocker’s (2017) \[originally exported by the [syuzhet] package\] & Rinker’s augmented Hu & Liu (2004) dictionaries in the [lexicon] package). Positive (w*i*, j, k+) and negative (w*i*, j, k−) words are tagged with a +1 and −1 respectively (or other positive/negative weighting if the user provides the sentiment dictionary). I will denote polarized words as p*w* for convenience. These will form a polar cluster (c*i*, j, l) which is a subset of the a sentence (c*i*, j, l ⊆ s*i*, j). The polarized context cluster (c*i*, j, l) of words is pulled from around the polarized word (pw) and defaults to 4 words before and two words after pw to be considered as valence shifters. The cluster can be represented as (c*i*, j, l = {pw*i*, j, k − nb, …​, pw*i*, j, k, …​, pw*i*, j, k − na}), where nb & na are the parameters n.before and n.after set by the user. The words in this polarized context cluster are tagged as neutral (w*i*, j, k0), negator (w*i*, j, k*n*), amplifier \[intensifier\] (w*i*, j, k*a*), or de-amplifier \[downtoner\] (w*i*, j, k*d*). Neutral words hold no value in the equation but do affect word count (n). Each polarized word is then weighted (w) based on the weights from the polarity_dt argument and then further weighted by the function and number of the valence shifters directly surrounding the positive or negative word (pw). Pause (cw) locations (punctuation that denotes a pause including commas, colons, and semicolons) are indexed and considered in calculating the upper and lower bounds in the polarized context cluster. This is because these marks indicate a change in thought and words prior are not necessarily connected with words after these punctuation marks. The lower bound of the polarized context cluster is constrained to max{pw*i*, j, k − nb, 1, max{cw*i*, j, k < pw*i*, j, k}} and the upper bound is constrained to min{pw*i*, j, k + na, w*i*, jn, min{cw*i*, j, k > pw*i*, j, k}} where w*i*, j*n* is the number of words in the sentence. The core value in the cluster, the polarized word is acted upon by valence shifters. Amplifiers increase the polarity by 1.8 (.8 is the default weight (z)). Amplifiers (w*i*, j, k*a*) become de-amplifiers if the context cluster contains an odd number of negators (w*i*, j, k*n*). De-amplifiers work to decrease the polarity. Negation (w*i*, j, k*n*) acts on amplifiers/de-amplifiers as discussed but also flip the sign of the polarized word. Negation is determined by raising −1 to the power of the number of negators (w*i*, j, k*n*) plus 2. Simply, this is a result of a belief that two negatives equal a positive, 3 negatives a negative, and so on. The adversative conjunctions (i.e., 'but', 'however', and 'although') also weight the context cluster. An adversative conjunction before the polarized word (w*adversative* conjunction, …​, w*i*, j, k*p*) up-weights the cluster by 1 z2 * {|*w**adversative* conjunction|,…​,w*i*, j, k*p*} (.85 is the default weight (z2) where |w*adversative* conjunction| are the number of adversative conjunctions before the polarized word). An adversative conjunction after the polarized word down-weights the cluster by 1 {w*i*, j, k*p*, …​, |w*adversative* conjunction|* − 1}\**z*2. This corresponds to the belief that an adversative conjunction makes the next clause of greater values while lowering the value placed on the prior clause. The researcher may provide a weight (z) to be utilized with amplifiers/de-amplifiers (default is .8; de-amplifier weight is constrained to −1 lower bound). Last, these weighted context clusters (c*i*, j, l) are summed (c′*i*, j) and divided by the square root of the word count (√w*i*, jn) yielding an unbounded polarity score** (δ*i*, j) for each sentence. w*neg* = (∑w*i*, j, k*n* ) mod 2. To get the mean of all sentences (s*i*, j) within a paragraph/turn of talk (p*i*) simply take the average sentiment score p*i*, δ*i*, j = 1/n ⋅ ∑ δ*i*, j or use an available weighted average (the default average_weighted_mixed_sentiment which upweights the negative values in a vector while also downweighting the zeros in a vector or average_downweighted_zero which simply downweights the zero polarity scores).