Sentence examples for most negative sentiment from inspiring English sources
(2017) analyze daily sentiment and purchase the 10% of firms with the most positive sentiment and short sell the 10% of firms with the most negative sentiment.
We checked the sentiment scores of the words that qualified for inclusion in our TF-IDF analysis, and found that he had the most negative sentiment score for individual words, as well.
Most mixed and negative sentiment for the sharing economy is based on concerns over safety (provider and consumer), fair business practices (equal application of regulations or "leveling the playing field"), or lost tax revenue (uncollected hotel taxes).
Each word in the lexicon has assigned a value representing sentiment in the range of −100 (most negative) to 100 (most positive).
Think of how Dell turned a tidal wave of online dissatisfaction into a brilliant coup and reversed negative sentiment by using social media channels to their most positive advantage, daring to let go of the controls a little.
Given these difficulties, investors may instead be most likely to react to how other investors are reacting, meaning that negative sentiment could feed back upon itself, causing extremely rapid changes in prices.
The most notable observation is that doubling the size of the training data, by adding negative sentiment training instances, did not improve results and, in fact, slightly lowered the accuracy of detecting positive sentiment spam reviews.
Failures in clinical trials, most notably the organ failures caused by the immunosuppressive drug candidate TGN1412, contributed their own negative sentiment.
Bosnia and Herzegovina had the most serious doubts about the effectiveness of vaccines, with a whopping 27.3% of respondents expressing a "negative sentiment".
The negative sentiment towards wildland fire prevailed and helped to shape wildland fire management objectives throughout most of the 20th century.
Most of the other approaches are based on tweet volume or simple sentiment analysis by counting positive and negative sentiment words in tweets.
