<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Quantifying the relationship between public sentiment and urban environment in Barcelona."
Abstract:"Public sentiment provides an important social reference for urban management and planning. The relationship between public sentiment and a single type of land use has yielded stable results in previous studies. Hitherto, there has been relatively little research on the correlation of the entire urban environment with public sentiment. Based on the unit of statistical area in Barcelona city, this research uses Twitter sentiment to represent public sentiment and develops a regression model for understanding the interrelationship of four layers: sociodemo-graphic, built-environment, human mobility and socioeconomic activities. The result shows that: 1) The long-term spatial difference in public sentiment has correlations with the urban environment, though it is not deci-sive. 2) Regardless of disruptive events that are directly associated with public sentiments, the wealthier areas show a more positive correlation with higher public sentiment. 3) The distribution of sentiment tweets (non -neutral) has a close relationship with places where there is a high flow of human activities. This study contributes to the systematic literature of urban applications of sentiment analysis with new empirical observations and a transferable methodology.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [0];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [1];
Q6 = [1].
Quantifying the relationship between public sentiment and urban environment in Barcelona
Public sentiment provides an important social reference for urban management and planning. The relationship between public sentiment and a single type of land use has yielded stable results in previous studies. Hitherto, there has been relatively little research on the correlation of the entire urban environment with public sentiment. Based on the unit of statistical area in Barcelona city, this research uses Twitter sentiment to represent public sentiment and develops a regression model for understanding the interrelationship of four layers: sociodemo-graphic, built-environment, human mobility and socioeconomic activities. The result shows that: 1) The long-term spatial difference in public sentiment has correlations with the urban environment, though it is not deci-sive. 2) Regardless of disruptive events that are directly associated with public sentiments, the wealthier areas show a more positive correlation with higher public sentiment. 3) The distribution of sentiment tweets (non -neutral) has a close relationship with places where there is a high flow of human activities. This study contributes to the systematic literature of urban applications of sentiment analysis with new empirical observations and a transferable methodology.